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  <title>Multimedia Annotation Interoperability Framework</title>
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    </a><a href="http://www.w3.org/2005/Incubator/XGR/"><img height="48" width="160" alt="W3C Incubator Report" src="http://www.w3.org/2005/Incubator/images/XGR" />
    </a>
   </p>
   <h1>Multimedia Annotation Interoperability Framework</h1>
   <h2><a id="w3c-doctype" name="w3c-doctype" />W3C Incubator Group Editor's Draft 
    14 August 2007</h2>
   <dl>
    <dt>This version:</dt>
    <dd>
     <a href="http://www.w3.org/2005/Incubator/mmsem/XGR-interoperability-20070814/">http://www.w3.org/2005/Incubator/mmsem/XGR-interoperability-20070814/</a></dd>
    <dt>Latest version:</dt><dd><a href="http://www.w3.org/2005/Incubator/mmsem/XGR-interoperability/">http://www.w3.org/2005/Incubator/mmsem/XGR-interoperability/</a></dd>
    <dt>Previous version:</dt>
    <dd>
     This is the first public version.
    </dd>
    <dt>Editor:</dt>
    <dd>
     <a href="http://www.image.ece.ntua.gr/~tzouvaras/">Vassilis Tzouvaras</a>, 
     IVML, National Technical University of Athens</dd>
    <dd>
     <a href="http://www.cwi.nl/~troncy/">Rapha&#235;l Troncy</a>, Center for 
     Mathematics and Computer Science (CWI Amsterdam)</dd>
    <dd>
     <a href="http://www.csd.abdn.ac.uk/~jpan/">Jeff Z. Pan</a>, University of 
     Aberdeen</dd>
    <dt>&#160; </dt>
    <dd>
     Also see <a href="#acknowledgments">Acknowledgements</a>.</dd>
   </dl>
   <p class="copyright">
    <a href="http://www.w3.org/Consortium/Legal/ipr-notice#Copyright">Copyright</a> 
    &#169; 2007 <a href="http://www.w3.org/">
     <acronym title="World Wide Web Consortium">W3C</acronym></a><sup>&#174;</sup> (<a href="http://www.csail.mit.edu/"><acronym title="Massachusetts Institute of Technology">MIT</acronym></a>,
    <a href="http://www.ercim.org/">
     <acronym title="European Research Consortium for Informatics and Mathematics">ERCIM</acronym></a>,
    <a href="http://www.keio.ac.jp/">Keio</a>), All Rights Reserved. W3C <a href="http://www.w3.org/Consortium/Legal/ipr-notice#Legal_Disclaimer">
     liability</a>, <a href="http://www.w3.org/Consortium/Legal/ipr-notice#W3C_Trademarks">
     trademark</a> and <a href="http://www.w3.org/Consortium/Legal/copyright-documents">
     document use</a> rules apply.
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  </div>
  <hr />
  <h2>
   <a id="abstract" name="abstract">Abstract </a>
  </h2>
  <p>
   Multimedia systems typically contain digital documents of mixed media types, 
   which are indexed on the basis of strongly divergent metadata standards. This 
   severely hamplers the inter-operation of such systems. Therefore, machine 
   understanding of metadata comming from different applications is a basic 
   requirement for the inter-operation of distributed Multimedia systems. In this 
   document, we present how interoperability among metadata, 
   vocabularies/ontologies and services is enhanced using Semantic Web 
   technologies. In addition, it provides guidelines for semantic 
   interoperability, illustrated by use cases. Finally, it presents an overview of 
   the most commonly used metadata standards and tools, and provides the general 
   research direction for semantic interoperability using Semantic Web 
   technologies.
  </p>
  <h2>
   <a id="status" name="status">Status of This Document</a>
  </h2>
  <p>
   <em>This section describes the status of this document at the time of its 
    publication. Other documents may supersede this document. A list of <a href="http://www.w3.org/2005/Incubator/XGR/">
     Final Incubator Group Reports</a> is available. See also the <a href="http://www.w3.org/TR/">
     W3C technical reports index</a> at http://www.w3.org/TR/. </em>
  </p>
  <p>
   This document was developed by the W3C <a href="http://www.w3.org/2005/Incubator/mmsem/">
    Multimedia Semantics Incubator Group</a>, part of the <a href="http://www.w3.org/2005/Incubator/">
    W3C Incubator Activity</a>.
  </p>
  <p>
   Publication of this document by W3C as part of the <a href="http://www.w3.org/2005/Incubator/">
    W3C Incubator Activity</a> indicates no endorsement of its content by W3C, nor 
   that W3C has, is, or will be allocating any resources to the issues addressed 
   by it. Participation in Incubator Groups and publication of Incubator Group 
   Reports at the W3C site are benefits of <a href="http://www.w3.org/Consortium/join">
    W3C Membership</a>.
  </p>
  <p>Incubator Groups have as a <a href="http://www.w3.org/2005/Incubator/procedures.html#Patent">
    goal</a> to produce work that can be implemented on a Royalty Free basis, as 
   defined in the W3C Patent Policy. Participants in this Incubator Group have 
   made no statements about whether they will offer licenses according to the <a href="http://www.w3.org/Consortium/Patent-Policy-20030520.html#sec-Requirements">
    licensing requirements of the W3C Patent Policy</a> for portions of this 
   Incubator Group Report that are subsequently incorporated in a W3C 
   Recommendation.
  </p>
  <h2>
   <a id="scope" name="scope">Scope </a>
  </h2>
  <p>
   This document targets at people with an interest in semantic interoperability, 
   ranging from non-professional end-users that have content are manually 
   annotating their personal digital photos to professionals working with digital 
   pictures in image and video banks, audiovisual archives, museums, libraries, 
   media production and broadcast industry, etc.
  </p>
  <p>
   Discussion of this document is invited on the public mailing list <a href="mailto:public-xg-mmsem@w3.org">
    public-xg-mmsem@w3.org</a> (<a href="http://lists.w3.org/Archives/Public/public-xg-mmsem/">public 
    archives</a>). Public comments should include "[MMSEM-Interoperability]" as 
   subject prefix .
  </p>
  <hr />
  <!-- ======================================================================== -->
  <div class="toc">
   <h2 class="notoc">
    <a id="contents" name="contents">Table of Contents</a>
   </h2>
   <ul id="toc" class="toc">
    <li class="tocline">
     <a href="#introduction"><b>1. Introduction in Semantic Interoperability in 
       Multimedia Applications</b></a>
    </li>
    <li class="tocline">
     <a href="#useCases"><b>2. Use Cases and Possible Solutions</b></a>
     <ul>
      <li class="tocline">
       <a href="#photo">2.1 Use Case: Photo</a></li>
      <li class="tocline">
       <a href="#music">2.2 Use Case: Music</a></li>
      <li class="tocline">
       <a href="#news">2.3 Use Case: News</a></li>
      <li class="tocline">
       <a href="#tagging">2.4 Use Case: Tagging</a></li>
      <li class="tocline">
       <a href="#semanticRetrieval">2.5 Use Case: Semantic Media Analysis for 
        Intelligent Retrieval</a></li>
      <li class="tocline">
       <a href="#algortihm">2.6 Use Case: Algorithm Representation</a></li>
     </ul>
    </li>
    <li class="tocline">
     <a href="#openIssues"><b>3. Open Issues</b></a>
     <ul>
      <li class="tocline">
       <a href="#authoring">3.1 Semantics From Multimedia Authoring </a>
      </li>
      <li class="tocline">
       <a href="#multimedial">3.2 Building Multimedial Semantic Web Applications </a>
      </li>
     </ul>
    </li>
    <li class="tocline">
     <a href="#framework"><b>4. Common Framework</b></a>
     <ul>
      <li class="tocline">
       <a href="#syntactic">4.1 Syntactic Interoperability: RDF</a></li>
      <li class="tocline">
       <a href="#layers">4.2 Layers of Interoperability</a></li>
      <li class="tocline">
       <a href="#common">4.3 Common Ontology/Schema</a></li>
      <li class="tocline">
       <a href="#ontology">4.4 Ontology/Schema Integration Harmonisation and Extensions</a></li>
      <li class="tocline">
       <a href="#guidelines">4.5 Guidelines</a></li>
     </ul>
    </li>
    <li class="tocline">
     <a href="#conclusion"><b>5. Conclusion</b></a></li>
    <li class="tocline">
     <a href="#references"><b>6. References</b></a></li>
    <li class="tocline">
     <a href="#acknowledgments"><b>Acknowledgments</b></a></li>
   </ul>
  </div>
  <!-- ======================================================================== -->
  <h2>
   <a name="introduction">1. Introduction in Semantic Interoperability in 
    Multimedia Applications</a>
  </h2>
  <p>
This document uses a bottom-up approach to provide a simple extensible framework to improve interoperability of
applications related to some key <a href="#useCases">use cases</a> discussed in the XG.
  </p>

  <div>
   <center>
     <img src="interoperability_use_case.png" alt="Use Cases Overview"/>
   </center>
  </div>

  <!-- ======================================================================== -->
  <h2>
   <a name="useCases">2. Use Cases and Possible Solutions</a>
  </h2>
  <p>
   In this section, we present several use cases showing interoperability problems 
   with multimedia metadata formats. Each use case starts with an example 
   illustrating the main problem, and proposed after a possible solution using 
   Semantic Web technologies.
  </p>

  <!-- ======================================================================== -->
  <h3>
   <a name="photo">2.1 Use Case: Photo</a>
  </h3>

  <h4 id="photo-introduction">Introduction</h4>
  <p>
   Currently, we are facing a market in which, for example, more than 20 billion digital photos 
   are taken per year in Europe [<a href="#GFK2006">GFK</a>]. 
   The number of tools, either for desktop machines or web-based, that perform 
   automatic as well as manual annotation of the content is 
   increasing. For example, a large number of personal photo management tools extract 
   information from the so called EXIF [<a href="#Exif">EXIF</a>] header and add 
   this information to the photo description. These tools typically allow to tag and describe 
   single photos. There are also many web-based tools that allow to upload photos to 
   share them, organize them and annotate them. Web sites such as [<a href="#Flickr">Flickr</a>] 
   allow tagging on the large scale. Sites like [<a href="#Riya">Riya</a>] 
   provide specific services such as face detection and face 
   recognition of personal photo collections. Photo community sites such as [<a href="#fotocommunity">Foto 
   Community</a>] allow an organization of the photos in categories and 
   allow rating and commenting on them. Even though our photos today find more and 
   more tools to manage and share them, these tools come with different 
   capabilities. What remains difficult is finding, sharing, reusing photo 
   collections across the borders of tools and sites. Not only the way in which 
   photos are automatically and manually annotated is different but also the 
   way in which this metadata is described and represented finds many different 
   standards. At the beginning of the management of personal photo collections is 
   the semantic understanding of the photos.
  </p>
  
  <h4 id="photo-scenario">Motivating Example</h4>
  <p>
   From the perspective of an end user let us consider the following scenario to 
   describe what is missing and needed for next generation digital photo services. 
   Ellen Scott and her family were on a nice two-week vacation in Tuscany. 
   They enjoyed the sun at the beaches of the Mediterranean, appreciating the 
   great culture in Florence, Siena and Pisa, and traveling on the traces of the 
   Etruscans through the small villages of the Maremma. During their marvelous 
   trip, the family was taking pictures of the sightseeing spots, the landscapes 
   and of course from the family members. The digital camera they use is already 
   equipped with a GPS receiver, so every photo is stamped not only with the time 
   when, but also with the geo-location where it has been taken.
  </p>
  
  <h5 id="photo-selection">Photo annotation and selection</h5>
  <p>
   Back home, the family uploads about 1000 pictures from the camera to the 
   computer and wants to create an album for grand dad. On this computer, the 
   family uses a photo management tool which both extracts some basic 
   features such as the EXIF header, but also allows for entering tags and personal 
   descriptions. Still fulfilled with the memories of the nice trip the mother of 
   the family labels most of the photos. With a second tool, the tour of the GPS 
   receiver and the photos are merged using the time stamp. As a results, each of 
   the photos is geo-referenced with the GPS position stored in the EXIF header. 
   However, showing all the photos would take an entire weekend. So Ellen starts 
   to create an excerpt of their trip with the highlights. Her 
   photo album software takes in the 1000 pictures and makes suggestions for the 
   selection and the arrangement of the pictures in a photo album. For example, 
   the album software shows her a map of Tuscany and visualises, where she has 
   taken which photos and groups them together making suggestions which photos 
   would best represent this part of the vacation. For places for which the 
   software detects highlights, the system offers to add information about the 
   place to the album, stating that on this Piazza in front of the Palazzo Vecchio 
   there is the copy of Michelangelo's famous David statue. Depending on the 
   selected style, the software creates a layout and the distribution of all 
   images over the pages of the album taking into account color, spatial and 
   temporal clusters and template preference. So, in about 20 minutes Ellen has 
   finished the album and orders a paper version as well as an online-version. The 
   paper album is delivered to her by mail three days later. It looks great, and 
   the explaining texts that her software has almost automatically added to the 
   pictures are informative and help her remembering the great vacation. They show 
   the album to grandpa and he can take his time to study their vacation and the 
   wonderful Tuscany.
  </p>
  
  <h5 id="photo-sharing">Exchanging and sharing photos</h5>
  <p>
   Selecting the most impressive photos, the son of the family uploads a nice set 
   of photos to <a href="#Flickr">Flickr</a>, to give his friends an impression of the great vacation. 
   Unfortunately, all the descriptions and annotations from the personal 
   photo management system are lost after the Web upload. Therefore, he adds a few 
   own tags to the Flickr photos to describe the places, events, persons of the 
   trip. Even the GPS track is lost and he places the photos again on the Flickr 
   map application to geo-reference them. One friend finds a cool picture from the 
   Spanish Stairs in Rome by night and would like to get the photo and its 
   location from Flickr. This is difficult again as a pure download of the photo 
   does not retain the geo-location. When aunt Mary visits the Web album and 
   starts looking on the photos she tries to download a few onto her laptop to 
   integrate them into her own photo management software. Now aunt Mary would like 
   to incorporate some of the pictures of her nieces and nephews into her photo 
   management system. And again, the system imports the photos but the precious 
   metadata that mother and sun of family Miller have already annotated twice are 
   gone.
  </p>

  <h4 id="photo-problem">The fundamental problem: semantic content understanding</h4>

  <div style="float: right; width: 45%; border: 1px solid gray; padding: 1%; margin: 1%">
     <img src="photo-indoor-outdoor.png" alt="Indoor/Outdoor detection with signal analysis and context analysis"/>
     <br/>
     Indoor/Outdoor detection with signal analysis and context analysis.<br/>
     Image courtesy of <a href="http://mmit.informatik.uni-oldenburg.de/en/">Susanne Boll</a>, used with permission.     
  </div>

  <p>
   What is needed is a better and more effective automatic annotation of digital 
   photos that better reflects one's personal memory of the events captured by the 
   photos an allows different applications to create value-added services on top 
   of them such as the creation of a personal photo album book. For understanding 
   the personal photos and overcoming the semantic gap, digital cameras leave us 
   with files like <tt>dsc5881.jpg</tt>, a very poor reflection of the actual event. Is is 
   a 2D visual snapshot of a multi-sensory personal experience. The quality of 
   photos is often very limited (snapshots, over exposed, blurred, ...). On the 
   other hand digital photos come with a large potential for semantic 
   understanding the photos. Photographs are always taken in context. In 
   contrast to analogical photography, digital photos provide us with explicit 
   contextual information (time, flash, aperture, ...), a "unique id" such as the 
   timestamp allows to later merge contextual information with the pure image 
   content.
  </p>

  <p>
   However, what we want to remember along with the photo is where it was, who was 
   there with us, what can be seen on the photo, what the weather was, if we liked 
   the event and so on. In recent years, it became clear that signal analysis 
   alone will not be the solution. In combination with the context of the photo, 
   such as the GPS position or the time stamp, some hard signal processing problems can be 
   solved better. So context analysis has gained much attention and became 
   important for photos and very helpful for photo understanding. 
   In the opposite figure, a simple example is given of how to 
   combine signal analysis and context analysis to achieve a better indoor/outdoor 
   detection of photos. And, not only with the advent of the Web 2.0 the actual 
   user came into focus. The manual effort of single user annotations but also 
   collaborative effects are considered to be important for semantic photo 
   understanding.
  </p>
  
  <p>
   The role of metadata for this usage of photo collections is manyfold:
  </p>
  <ul>
   <li>
    Save the experience: The central goal is to overcome the semantic gap and 
    represent as much of the humans impression of the moment when the photo was 
    taken.</li>
   <li>
    Browse and find previously taken photos: Allow searching for events and 
    persons, places, moments in time, etc.</li>
   <li>
    Share photos with the metadata with others: Give your annotated photos from 
    Flickr or from Foto Community to your friends' applications.</li>
   <li>
    Use comprehensive metadata for value-added services of the photos: Create an 
    automatic photo collage or send a flash presentation to your aunt&#xe2;&#x80;&#x99;s TV, notify 
    all friends that are interested in photos from certain locations, events, or 
    persons, etc.</li>
  </ul>

  <div style="float: right; width: 45%; border: 1px solid gray; padding: 1%; margin: 1%">
     <img src="photo-usage.png" alt="Photos usage"/>
     <br/>
     Photos usage.<br/>
     Image courtesy of <a href="http://mmit.informatik.uni-oldenburg.de/en/">Susanne Boll</a>, used with permission.
  </div>

  <p>
   The opposite figure illustrates the use of photos today and what we do with 
   our photos at home but also in the Web.
  </p>
  
  <p>
   So the social life of personal photos can be summarized as:
  </p>
  <ul>
   <li>
    Capturing: one or more persons capture and event, with one or different cameras 
    with different capabilities and characteristics</li>
   <li>
    Storing: one or more persons store the photos with different tools on different 
    systems</li>
   <li>
    Processing: post-editing with different tools that change the quality and maybe 
    the metadata</li>
   <li>
    Uploading: some persons make their photos available on Web (2.0) sites 
    (Flickr); different sites offer different kinds of value-added services to the 
    photos (Riya)</li>
   <li>
    Sharing: photos are given away or are given access to via email, Web sites, 
    print, ...</li>
   <li>
    Receiving: photos from others are received via MMS, email, download, ...
   </li>
   <li>
    Combining: Photos from own and different sources are selected and reused for 
    services like T-Shirt, Mugs, mouse pads, photo albums, collages, ...
   </li>
  </ul>
  <p style="clear: both">
   For this, metadata plays a central role at all times and places of the social 
   life of our photos.
  </p>

  <h4 id="photo-interoperability">The multimedia semantics interoperability problem</h4>

  <h5 id="photo-metadata-level">Different levels and types of metadata for photos</h5>
  <p>
   The problem we have here is that metadata is created and 
   enhanced by different tools and systems and follows different standards and 
   representations. Even though there are many tools and standards that aim 
   to capture and maintain this metadata, they are not necessarily interoperable. 
   So on a technical level, we have the problem of a common representation of 
   metadata that is helpful and relevant for photo management, sharing and reuse. 
   Metadata and end user typically gets in touch with descriptive metadata that 
   stem from the context of the photo. At the same time, in more than a decade 
   many results in multimedia analysis have been achieved to extract many 
   different valuable features from multimedia content. For photos for example, 
   this includes color histograms, edge detection, brightness, texture and so on. 
   With <a href="#MPEG-7">MPEG-7</a>, a very large standard has been developed that allows to describe 
   these features in a standardized way. However, both the size of the standard but also the many 
   optional attributes in the standard have lead to a situation in which MPEG-7 is 
   used only in very specific applications and has not been achieved as a world 
   wide accepted standard for adding (some) metadata to a media item. Especially 
   in the area of personal media, in the same fashion as in the tagging scenario, 
   a small but comprehensive shareable and exchangeable description scheme for 
   personal media is missing.
  </p>
  
  <h5 id="photo-metadata-standard">Different standards for photo metadata and annotations</h5>
  <p>
   What is needed is a machine readable description that comes with each photo 
   that allows a site to offer valuable search and selection functionality on the 
   uploaded photos. Even though approaches for Photo Annotation have been proposed 
   they still do not address the wide range of metadata, annotations that could 
   and should be stored with an image in a standardized fashion.
  </p>
  <ul>
   <li>
    EXIF [<a href="#Exif">EXIF</a>] is a standard that comprises many photographic 
    and capture relevant metadata. Even though the end user might use only a few of 
    the key/value pairs, they are relevant at least for photo editing and archiving 
    tools which read this kind of metadata and visualize it. So EXIF is a necessary 
    set of metadata which is needed for photos.
   </li>
   <li>
    Tags from Flickr and other photo web sites and tools are metadata of low 
    structure but high relevance for the user and the use of the photos. Manually 
    added they reflect the users knowledge and understanding of the content which 
    can not be replaced by any automatic semantic extraction. Therefore, a 
    representation of these is needed. Depending on the source of tags is might be 
    of interest to relate the tags to their origin such as &quot;taken from an 
    existing vocabulary&quot;, &quot;from a suggested set of other tags&quot; or 
    just &quot;free tags&quot;. XMP seems to be a very promising standard as it 
    allows to define RDF-based metadata for photos. However, in the description of 
    the standard, it clearly states that it leaves the application dependent schema 
    /vocabulary definition to the application and only makes suggestions for a set 
    of &quot;generic&quot; sets such as EXIF, Dublin Core. So the standard could be 
    a good &quot;host&quot; for a defined photo metadata description scheme in RDF 
    but does not define it.</li>
   <li>
    PhotoRDF [<a href="#PhotoRDF">PhotoRDF</a>] &quot;describes a project for 
    describing &amp; retrieving (digitized) photos with (RDF) metadata. It 
    describes the RDF schemas, a data-entry program for quickly entering metadata 
    for large numbers of photos, a way to serve the photos and the metadata over 
    HTTP, and some suggestions for search methods to retrieve photos based on their 
    descriptions.&quot; So wonderful, but the standard is separated into three 
    different schemas: Dublin Core [<a href="#DublinCore">Dublin Core</a>], a Technical Schema which 
    comprises more or less entries about author, camera and short description, and 
    a Content Schema which provides a set of 10 keywords. With PhotoRDF, the type 
    and number of attributes is limited, does not even comprise the full EXIF 
    schema and is also limited with regard to the content description of a photo.
   </li>
   <li>
    The Extensible Metadata Platform or XMP [<a href="#XMP">XMP</a>] and the 
    IPTC-IIM-Standard [<a href="#IIM">IIM</a>] have been introduced to define how 
    metadata (not only) of a photo can be stored with the media element itself. 
    However, these standards come with their own set of attributes to describe the 
    photo or allow to define individual metadata templates. This is the killer for 
    any sharing and semantic Web search! What is missing is an actual standardized 
    vocabulary what information about a photo is important and relevant to a large 
    set of next generation digital photo services has not been reached.
   </li>
   <li>
    The Image Annotation on the Semantic Web [<a href="#MMSEM-Image">MMSEM Image</a>] 
    provides an overview of the existing standard such as those mentioned 
    above. At the same time it shows how diverse the world of annotation is. The 
    use case for photo annotation choses RDF/XML syntax of RDF in order to gain 
    interoperability. It refers to a large set of different standards and 
    approaches that can be used to image annotation but there is no unified view on 
    image annotation and metadata relevant for photos. The attempt here is to 
    integrated existing standards. If those however are too many, too 
    comprehensive, and might even have overlapping attributes is might not be 
    adopted as the common photo annotation scheme on the Web. For example, for the 
    low level features for example, there is only a link to MPEG-7.
   </li>
   <li>
    The DIG35 Initiative Group of the International Imaging Industry Association 
    aims &quot;provide a standardized mechanism which allows end-users to see 
    digital image use as being equally as easy, as convenient and as flexible as 
    the traditional photographic methods while enabling additional benefits that 
    are possible only with a digital format.&quot; [<a href="#DIG35">DIG35</a>]. 
    The DIG35 standards aims to define a standard set of metadata for digital 
    images that can be widely implemented across multiple image file formats. From 
    all the photo standards this is the broadest one with respect to typical photo 
    metadata and is already defined as a XML Schema.</li>
   <li>
    MPEG-7 is far to big even though the standard comprises metadata elements that 
    are relevant also for a Web wide usage of media content. The advantage of 
    MPEG-7 is that one can define an own description scheme and with it collect a 
    subset of relevant feature related metadata with a photo. But, there is no 
    chance to actually include an entire XML-based MPEG-7 description of a photo 
    into the raw content. For the description of the content the use case refers to 
    three domain-specific ontologies: personal history event, location and 
    landscape.</li>
  </ul>
  
  <h4 id="photo-solution">Towards a solution</h4>

  <div style="float: right; width: 45%; border: 1px solid gray; padding: 1%; margin: 1%">
     <img src="photo-interoperability.png" alt="Toward a solution for photo metadata interoperability"/>
     <br/>
     Toward a solution for photo metadata interoperability.<br/>
     Image courtesy of <a href="http://mmit.informatik.uni-oldenburg.de/en/">Susanne Boll</a>, used with permission.
  </div>

  <p>
   The result is clear, that there is not one standardized representation and 
   vocabulary for adding metadata to photos. Even though the different semantic 
   Web applications and developments should be embraced, a photo annotation 
   standard as a patchwork of too many different specifications is not helpful. 
   The opposite Figure illustrates some of the different actitivities, as 
   described aboce in the scenario, that people do with their photos and what 
   different standalone or web-based tools they use for this.
  </p>

  <p>
   What is missing, however, for content management, search, retrieval, sharing 
   and innovative semantic (Web 2.0) applications is a limited and simple but at 
   the same time comprehensive vocabulary in a machine-readable, exchangeable, but 
   not over complicated representation is needed. However, the single standards 
   described only solve part of the problem. For example, a standardization of 
   tags is very helpful for a semantic search on photos in the Web. However, today 
   the low(er) level features are also lost. Even though the semantic search is 
   fine on a search level, for a later use and exploitation of a set of photos, 
   previously extracted and annotated lower-level features might be interesting as 
   well. Maybe a Web site would like to offer a grouping of photos along the color 
   distribution. Then either the site needs to do the extraction of a color 
   histogram or the photo itself brings this information already in in its 
   standardized header information. A face detection software might have found the 
   bounding boxes on the photo where a face has been detected and also provide a 
   face count. Then the Web site might allow to search for photos with two or more 
   persons on it. And so one. Even though low level features do not seem relevant 
   at first sight, for a detailed search, visualization and also later processing 
   the previously extracted metadata should be stored and available with the 
   photo.
  </p>

  <!-- TO ADD:
    * OWL/RDFS Schema for DIG35 and MPEG-7
  -->

  <!-- ======================================================================== -->
  <h3>
   <a name="music">2.2 Use Case: Music</a>
  </h3>

  <h4 id="music-introduction">Introduction</h4>
  <p>
   In recent years the typical music consumption behaviour has changed 
   dramatically. Personal music collections have grown favoured by technological 
   improvements in networks, storage, portability of devices and Internet 
   services. The amount and availability of songs has de-emphasized its value: it 
   is usually the case that users own many digital music files that they have only 
   listened to once or even never. It seems reasonable to think that by providing 
   listeners with efficient ways to create a personalized order on their 
   collections, and by providing ways to explore hidden "treasures" inside them, 
   the value of their collection will drastically increase.
  </p>
  <p>Also, notwithstanding the digital revolution had many advantages, we can 
   point out some negative effects. Users own huge music collections that need 
   proper storage and labelling. Searching inside digital collections arise new 
   methods for accessing and retrieving data. But, sometimes there is no metadata 
   -or only the file name- that informs about the content of the audio, and that 
   is not enough for an effective utilization and navigation of the music 
   collection.
  </p>
  <p>Thus, users can get lost searching into the digital pile of his music 
   collection. Yet, nowadays, the web is increasingly becoming the primary source 
   of music titles in digital form. With millions of tracks available from 
   thousands of websites, finding the right songs, and being informed of newly 
   music releases is becoming a problematic task. Thus, web page filtering has 
   become necessary for most web users.
  </p>
  <p>Beside, on the digital music distribution front, there is a need to find ways 
   of improving music retrieval effectiveness. Artist, title, and genre keywords 
   might not be the only criteria to help music consumers finding music they like. 
   This is currently mainly achieved using cultural or editorial metadata ("artist 
   A is somehow related with artist B") or exploiting existing purchasing 
   behaviour data ("since you bought this artist, you might also want to buy this 
   one"). A largely unexplored (and potentially interesting) complement is using 
   semantic descriptors automatically extracted from the music audio files. These 
   descriptors can be applied, for example, to recommend new music, or generate 
   personalized playlists.
  </p>

  <h4 id="music-description">A complete description of a popular song</h4>
  <p>In <a href="#Pachet">[Pachet]</a>, Pachet classifies the music 
   knowledge management. This classification allows to create meaningful 
   descriptions of music, and to exploit these descriptions to build music related 
   systems. The three categories that Pachet defines are: editorial (EM), cultural 
   (CM) and acoustic metadata (AM).
  </p>
  <p>Editorial metadata includes simple creation and production information (e.g. 
   the song C'mon Billy, written by P.J. Harvey in 1995, was produced by John 
   Parish and Flood, and the song appears as the track number 4, on the album "To 
   bring you my love"). EM includes, in addition, artist biography, album reviews, 
   genre information, relationships among artists, etc. As it can be seen, 
   editorial information is not necessarily objective. It is usual the case that 
   different experts cannot agree in assigning a concrete genre to a song or to an 
   artist. Even more diffcult is a common consensus of a taxonomy of musical 
   genres.
  </p>
  <p>Cultural metadata is defined as the information that is implicitly present in 
   huge amounts of data. This data is gathered from weblogs, forums, music radio 
   programs, or even from web search engines' results. This information has a 
   clear subjective component as it is based on personal opinions.
  </p>
  <p>The last category of music information is acoustic metadata. In this context, 
   acoustic metadata describes the content analysis of an audio file. It is 
   intended to be objective information. Most of the current music content 
   processing systems operating on complex audio signals are mainly based on 
   computing low-level signal features. These features are good at characterising 
   the acoustic properties of the signal, returning a description that can be 
   associated to texture, or at best, to the rhythmical attributes of the signal. 
   Alternatively, a more general approach proposes that music content can be 
   successfully characterized according to several "musical facets" (i.e. rhythm, 
   harmony, melody, timbre, structure) by incorporating higher-level semantic 
   descriptors to a given feature set. Semantic descriptors are predicates that 
   can be computed directly from the audio signal, by means of the combination of 
   signal processing, machine learning techniques, and musical knowledge.
  </p>
  <p>Semantic Web languages allow to describe all this metadata, as well as 
   integrating it from different music repositories.
  </p>
  <p>The following example shows an RDF description of an artist, and a song by 
   the artist:</p>

  <div class="exampleInner" style="clear: both">
   <pre>&lt;rdf:Description rdf:about="http://www.garageband.com/artist/randycoleman"&gt;
  &lt;rdf:type rdf:resource="&amp;music;Artist"/&gt;
  &lt;music:name&gt;Randy Coleman&lt;/music:name&gt;
  &lt;music:decade&gt;1990&lt;/music:decade&gt;
  &lt;music:decade&gt;2000&lt;/music:decade&gt;
  &lt;music:genre&gt;Pop&lt;/music:genre&gt;
  &lt;music:city&gt;Los Angeles&lt;/music:city&gt;
  &lt;music:nationality&gt;US&lt;/music:nationality&gt;
  &lt;geo:Point&gt;
    &lt;geo:lat&gt;34.052&lt;/geo:lat&gt;
    &lt;geo:long&gt;-118.243&lt;/geo:long&gt;
  &lt;/geo:Point&gt;
  &lt;music:influencedBy rdf:resource="http://www.coldplay.com"/&gt;
  &lt;music:influencedBy rdf:resource="http://www.jeffbuckley.com"/&gt;
  &lt;music:influencedBy rdf:resource="http://www.radiohead.com"/&gt;
&lt;/rdf:Description&gt;

&lt;rdf:Description rdf:about="http://www.garageband.com/song?|pe1|S8LTM0LdsaSkaFeyYG0"&gt;
  &lt;rdf:type rdf:resource="&amp;music;Track"/&gt;
  &lt;music:title&gt;Last Salutation&lt;/music:title&gt;
  &lt;music:playedBy rdf:resource="http://www.garageband.com/artist/randycoleman"/&gt;
  &lt;music:duration&gt;T00:04:27&lt;/music:duration&gt;
  &lt;music:key&gt;D&lt;/music:key&gt;
  &lt;music:keyMode&gt;Major&lt;/music:keyMode&gt;
  &lt;music:tonalness&gt;0.84&lt;/music:tonalness&gt;
  &lt;music:tempo&gt;72&lt;/music:tempo&gt;
&lt;/rdf:Description
</pre>
  </div>

  <h5 id="music-lyrics">Lyrics as metadata</h5>
  <p>For a complete description of a song, lyrics must be considered as well. 
   While lyrics could in a sense be regarded as "acoustic metadata", they are per 
   se actual information entities which have themselves annotation needs. Lyrics 
   share many similarities with metadata, e.g. they usually refer directly to well 
   specified song, but acceptions exists as different artist might sing the same 
   lyrics sometimes even with different musical bases and styles. Most notably, 
   lyrics have often different authors than the music and voice that interprets 
   them and might be composed at a different time. Lyrics are not a simple text; 
   they often have a structure which is similar to that of the song (e.g. a 
   chorus) so they justify the use use of a markup language with a well specified 
   semantics. Unlike the previous types of metadata, however, they are not well 
   suited to be expressed using the W3C Semantic Web initiative languages, e.g. in 
   RDF. While RDF has been suggested instead of XML for for representig texts in 
   situation where advanced and multilayered markup is wanted [Ref RDFTEI], music 
   lyrics markup needs usually limit themselves to indicating particular sections 
   of the songs (e.g. intro, outro, chorus) and possibly the performing character 
   (e.g. in duets). While there is no widespread standard for machine encoded 
   lyrics, some have been proposed [LML][4ML] which in general fit the need for 
   formatting and differentiating main parts. An encoding in RDF of lyrics would 
   be of limited use but still possible with RDF based queries possible just 
   thanks to text search operators in the query language (therfore likely to be 
   limited to "lyrics that contain word X"). More complex queries could be 
   possible if more characters are performing in the lirics and each denoted by an 
   RDF entity which has other metadata attached to it (e.g. the metadata described 
   in the examples above).
  </p>
  <p>It is to be reported however that an RDF encoding would have the disadvantage 
   of complexity. In general it would require a supporting software (for example <a href="http://rdftef.sourceforge.net/">
    http://rdftef.sourceforge.net/</a>) to be encoded as XML/RDF can be 
   difficultly written by hand. Also, contrary to an XML based encoding, it could 
   not be easily visualized in a human readable way by, e.g., a simple XSLT 
   transformation.
  </p>
  <p>Both in case of RDF and XML encoding, interesting processing and queries 
   (e.g. conceptual similarities between texts, moods etc) would necessitate 
   advanced textual analysis algorithms well outside the scope or XML or RDF 
   languages. Interestingly however, it might be possible to use RDF description 
   to encode the results of such advanced processings. Keyword extraction 
   algorithms (usually a combination of statistical analysis, stemming and 
   linguistical processing e.g. using wordnet) can be successfully employed on 
   lyrics. The resulting reppresentative "terms" can be encoded as metadata to the 
   lyrics or to the related song itself.
  </p>
  
  <h5 id="music-low-level">Lower Level Acoustic metadata</h5>
  <p>"Acoustic metadata" is a broad term which can encompass both features which 
   have an immediate use in higher level use cases (e.g. those presented in the 
   above examples such as tempo, key, keyMode etc ) and those that can only be 
   interpreted by data analisys (e.g. a full or simplified representation of the 
   spectrum or the average power sliced every 10 ms). As we have seen, semantic 
   technologies are suitable for reppresenting the higher level acoustic metadata. 
   These are in fact both concise and can be used directly in semantic queries 
   using, e.g., SparQL. Lower level metadata however, e.g. the MPEG7 features 
   extracted by extractors like [Ref MPEG7AUDIODB] is very ill suited to be 
   represented in RDF and is better kept in mpeg-7/xml format for serialization 
   and interchange.
  </p>
  <p>Semantic technologies could be of use in describing such "chunks" of low 
   level metadata, e.g. describing what the content is in terms of describing 
   which features are contained and at which quality. While this would be a 
   duplicaiton of the information encoded in the MPEG-7/XML, it might be of use in 
   semantic queries which select tracks also based on the availability of rich low 
   level metadata.</p>
   
  <h4 id="music-scenario">Motivating Example</h4>

  <div style="float: right; width: 45%; border: 1px solid gray; padding: 1%; margin: 1%">
     <img src="music-nextgig.png" alt="The next gig"/>
     <br/>
     The next gig.<br/>
     Image courtesy of <a href="http://www.iua.upf.es/~ocelma/">Oscar Celma</a>, used with permission.
  </div>

  <p>Commuting is a big issue in any modern society. Semantically Personalized 
   Playlists might provide both relief and actually benefit in time that cannot be 
   devoted to actively productive activities. Filippo commutes every morning an 
   average of 50+-10 minutes. Before leaving he connects his USB stick/mp3 player 
   to have it "filled" with his morning playlist. The process is completed in 10 
   seconds, afterall is just 50Mb. he is downloading. During the time of his 
   commute, Filippo will be offered a smooth flow of news, personal daily , 
   entertainment, and cultural snippets from audiobooks and classes.
  </p>
  <p>Musical content comes from Filippo personal music collection or via a content 
   provider (e.g. a low cost thanks to a one time pay license). Further audio 
   content comes from podcasts but also from text to speech reading blog posts, 
   emails, calendar items etc.
  </p>
  <p>Behind the scenes the system works by a combination of semantic queries and 
   ad-hoc algorithms. Semantic queries operate on an RDF database collecting the 
   semantic reppresentation of music metadata (as explained in section 1), as well 
   as annotations on podcasts, news items, audiobooks, and "semantic desktop 
   items" that is represting Filippo's personal desktop information -such as 
   emails and calendar entries.
  </p>
  <p>Ad-hoc algorithms operate on low level metadata to provide smooth transition 
   among tracks. Algorithms for text analysis provide further links among songs 
   and links within songs, pieces of news, emails etc.
  </p>
  <p>At a higher level, a global optimization algorithm takes care of the final 
   playlist creation. This is done by balancing the need for having high priority 
   items played first (e.g. emails from addresss considered important) with the 
   overall goal of providing a smooth and entertaining experience (e.g. 
   interleaving news with music etc).
  </p>
  <p>Semantics can help in providing "related information or content" which can be 
   put adjacent to the actual core content. This can be done in relative freedom 
   since the content can be at any time skipped by the user using simply the 
   forward button.</p>
   
  <h5 id="music-upcoming-concerts">Upcoming concerts</h5>
    
  <p>John has been listening to the "Snow Patrol" band for a while. He discovered 
    the band while listening to one of his favorite podcasts about alternative 
    music. He has to travel to San Diego next week, and he is finding upcoming 
    concerts that he would enjoy there, and he asks his personalized semantic web 
    music service to provide him with some recommendations of upcoming gigs in the 
    area, and decent bars to have a beer.
  </p>

  <div class="exampleInner">
    <pre>
&lt;!-- San Diego geolocation --&gt;
&lt;foaf:based_near geo:lat='32.715' geo:long='-117.156'/&gt;
    </pre>
  </div>
   
  <p>The system is tracking user listening habits, so it detects than one song 
    from "The Killers" band (scrapped from their website) sounds similar to the 
    last song John has listened to from "Snow Patrol". Moreover, both bands have 
    similar styles, and there are some podcasts that contain songs from both bands 
    in the same session. Interestingly enough, the system knows that the Killers 
    are playing close to San Diego next weekend, thus it recommends to John to 
    assist to that gig.</p>

  <h5 id="music-facet">Facet browsing of Music Collections</h5>
  <p>Michael has a brand new (last generation-posh) iPod. He is looking for some 
     music using the classic hierarchical navigation 
     (Genre-&gt;Artist-&gt;Album-&gt;Songs). But the main problem is that he is not 
     able to find a decent list of songs (from his 100K music collection) to move 
     into his iPod. On the other hand, facet browsing has recently become popular as 
     a user friendly interface to data repositories.
   </p>
   <p>/facet system <a href="#Hildebrand">[Hildebrand]</a> presents a new and intuitive way to navigate large 
     collections, using several facets or aspects, of multimedia assets. /facet 
     extends browsing of Semantic Web data in four ways. First, users are able to 
     select and navigate through facets of resources of any type and to make 
     selections based on properties of other, semantically related, types. Second, 
     it addresses a disadvantage of hierarchy-based navigation by adding a keyword 
     search interface that dynamically makes semantically relevant suggestions. 
     Third, the /facet interface, allows the inclusion of facet-specific display 
     options that go beyond the hierarchical navigation that characterizes current 
     facet browsing. Fourth, the browser works on any RDF dataset without any 
     additional configuration.
   </p>
   <p>Thus, based on a RDF description of music titles, the user 
     can navigate through music facets, such as Rhythm (beats per minute), Tonality 
     (Key and mode), Intensity of the piece (moderate, energetic, etc.)
   </p>
   <p>A fully functional example can be seen at <a href="http://slashfacet.semanticweb.org/music/mazzle">
      http://slashfacet.semanticweb.org/music/mazzle</a>
   </p>    
   <div style="border: 1px solid gray; padding: 1%; margin: 1%">
    <center>
     <img src="music-mazzle.png" alt="The Mazzle Interface"/>
     <br/>
     The Mazzle Interface.<br/>
     Image courtesy of <a href="http://www.cwi.nl/~hildebra/">Michiel Hildebrand</a>, used with permission.
    </center>
   </div>
     
   <h4 id="music-metadata">Music Metadata on the Semantic Web</h4>
   <p>Nowadays, in the context of the World Wide Web, the increasing amount of 
     available music makes very difficult, to the user, to find music he/she would 
     like to listen to. To overcome this problem, there are some audio search 
     engines that can fit the user's needs (for example: <a href="http://search.singingfish.com/">
      http://search.singingfish.com/</a>, <a href="http://audio.search.yahoo.com/">http://audio.search.yahoo.com/</a>,
     <a href="http://www.audiocrawler.com/">http://www.audiocrawler.com/</a>, <a href="http://www.alltheweb.com/?cat=mp3">
      http://www.alltheweb.com/?cat=mp3</a>, <a href="http://www.searchsounds.net">http://www.searchsounds.net</a>
     and <a href="http://www.altavista.com/audio/">http://www.altavista.com/audio/</a>).
   </p>
   <p>Some of the current existing search engines are nevertheless not fully 
     exploited because their companies would have to deal with copyright infringing 
     material. Music search engines have a crucial component: an audio crawler, that 
     scans the web and gathers related information about audio files.
   </p>
   <p>Moreover, describing music it not an easy task. As presented in section 1, 
     music metadata copes with several categories (editorial, acoustic, and 
     cultural). Yet, none of the audio metadata used in practice (e.g ID3, OGG 
     Vorbis, etc.) can fully describe all these facets. Actually, metadata for 
     describing music are mostly tags implemented in the Key-Value form 
     [TAG]=[VALUE], for instance, "ARTIST=The Killers".
   </p>
   <p>The following section introduces, then, the mappings between current audio 
     vocabularies within the Semantic Web technologies. This will allow to extend 
     the description of a piece of music, as well as adding explicit semantics.
   </p>
   
   <h4 id="music-integration">Integrating Various Vocabularies Using RDF</h4>
   <p>In this section we present a way to integrate several audio vocabularies into 
     a single one, based on RDF. For more details about the audio vocabularies, the 
     reader is refered to <a href="http://www.w3.org/2005/Incubator/mmsem/wiki/Vocabularies#head-91ffc7bd57a4631807ae03b31721b099db56937a">
      Vocabularies - Audio Content Section</a>, and <a href="http://www.w3.org/2005/Incubator/mmsem/wiki/Vocabularies#head-7d4cf55c8883fbcbfdbbe8b1eb1b1512c2a5b328">
      Vocabularies - Audio Ontologies Section</a>.
   </p>
   <p>This section will focus on the ID3 and OGG Vorbis metadata initiatives, as 
     they are the most used ones. Though, both vocabularies cope only editorial 
     data. Moreover, a first mapping with the <a href="http://www.w3.org/2005/Incubator/mmsem/wiki/Vocabularies#head-7d4cf55c8883fbcbfdbbe8b1eb1b1512c2a5b328">
      Music Ontology</a> is presented, too.
   </p>

   <p><a href="http://www.id3.org">ID3</a> is a metadata container most often 
     used in conjunction with the MP3 audio file format. It allows information such 
     as the title, artist, album, track number, or other information about the file 
     to be stored in the file itself (from Wikipedia).
    </p>
   <p>The most important metadata descriptors are:</p>
   <ul>
    <li>
      Artist name &lt;=&gt; <tt>&lt;foaf:name&gt;&lt;/foaf:name&gt;</tt>
    </li>
    <li>
      Album name &lt;=&gt; <tt>&lt;mo:Record&gt;&lt;dc:title&gt;Album 
       name&lt;/dc:title&gt;&lt;/mo:Record&gt;</tt>
    </li>
    <li>
      Song title &lt;=&gt; <tt>&lt;mo:Track&gt;&lt;dc:title&gt;Album 
       name&lt;/dc:title&gt;&lt;/mo:Track&gt;</tt>
    </li>
    <li>
      Year
    </li>
    <li>
      Track number &lt;=&gt; <tt>&lt;mo:trackNum&gt;Track 
       number&lt;/mo:trackNum&gt;&lt;/mo:Track&gt;</tt>
    </li>
    <li>
      Genre (from a predefined list of more than 100 genres) &lt;=&gt; <tt>&lt;mo:Genre&gt;Genre 
       name&lt;/mo:Genre&gt;</tt>
    </li>
   </ul>

   <p><a href="http://www.vorbis.com/">OGG Vorbis</a> metadata, called comments, 
     support metadata 'tags' similar to those implemented in the ID3. The metadata 
     is stored in a vector of strings, encoded in UTF-8
   </p>
   <ul>
    <li>
      TITLE &lt;=&gt; <tt>&lt;mo:Track&gt;&lt;dc:title&gt;Album 
       name&lt;/dc:title&gt;&lt;/mo:Track&gt;</tt>
    </li>
    <li>
      VERSION: The version field may be used to differentiate multiple versions of 
      the same track title
    </li>
    <li>
      ALBUM &lt;=&gt; <tt>&lt;mo:Record&gt;&lt;dc:title&gt;Album 
       name&lt;/dc:title&gt;&lt;/mo:Record&gt;</tt>
    </li>
    <li>
      TRACKNUMBER &lt;=&gt; <tt>&lt;mo:trackNum&gt;Track 
       number&lt;/mo:trackNum&gt;&lt;/mo:Track&gt;</tt>
    </li>
    <li>
      ARTIST &lt;=&gt; <tt>&lt;foaf:name&gt;&lt;/foaf:name&gt;</tt>
    </li>
    <li>
      PERFORMER &lt;=&gt; <tt>&lt;foaf:name&gt;&lt;/foaf:name&gt;</tt> ???
    </li>
    <li>
      COPYRIGHT: Copyright attribution
    </li>
    <li>
      LICENSE: License information, eg, 'All Rights Reserved', 'Any Use Permitted', a 
      URL to a license such as a Creative Commons license
    </li>
    <li>
      ORGANIZATION: Name of the organization producing the track (i.e &#xe2;&#x80;&#x98;a record 
      label&#xe2;&#x80;&#x99;)
    </li>
    <li>
      DESCRIPTION: A short text description of the contents
    </li>
    <li>
      GENRE &lt;=&gt; <tt>&lt;mo:Genre&gt;Genre name&lt;/mo:Genre&gt;</tt>
    </li>
    <li>
      DATE
    </li>
    <li>
      LOCATION: Location where the track was recorded</li>
   </ul>

   <h4 id="music-rdfizing">RDFizing songs</h4>
   <p>We present a way to RDFize tracks based on the 
   <a href="http://www.w3.org/2005/Incubator/mmsem/wiki/Vocabularies#head-7d4cf55c8883fbcbfdbbe8b1eb1b1512c2a5b328">
      Music Ontology</a>.
   </p>
   <p>Example: Search a song into <a href="http://www.musicbrainz.org">MusicBrainz</a>
      and RDFize results. This first example shows how to query the MusicBrainz music repository, and RDFize the results based on the Music 
      Ontology. Try a complete example at <a href="http://foafing-the-music.iua.upf.edu/RDFize/track?artist=U2&amp;title=The+fly">
      http://foafing-the-music.iua.upf.edu/RDFize/track?artist=U2&amp;title=The+fly</a>. 
      The parameters are song title (The Fly) and artist name (U2).
   </p>
   <div class="exampleInner" style="clear: both">
     <pre>&lt;mo:Track rdf:about='http://musicbrainz.org/track/dddb2236-823d-4c13-a560-bfe0ffbb19fc'&gt;
  &lt;mo:puid rdf:resource='2285a2f8-858d-0d06-f982-3796d62284d4'/&gt;
  &lt;mo:puid rdf:resource='2b04db54-0416-d154-4e27-074e8dcea57c'/&gt;
  &lt;dc:title&gt;The Fly&lt;/dc:title&gt;
  &lt;dc:creator&gt;
    &lt;mo:MusicGroup rdf:about='http://musicbrainz.org/artist/a3cb23fc-acd3-4ce0-8f36-1e5aa6a18432'&gt;
      &lt;foaf:img rdf:resource='http://ec1.images-amazon.com/images/P/B000001FS3.01._SCMZZZZZZZ_.jpg'/&gt;
      &lt;mo:musicmoz rdf:resource='http://musicmoz.org/Bands_and_Artists/U/U2/'/&gt;
      &lt;foaf:name&gt;U2&lt;/foaf:name&gt;
      &lt;mo:discogs rdf:resource='http://www.discogs.com/artist/U2'/&gt;
      &lt;foaf:homepage rdf:resource='http://www.u2.com/'/&gt;
      &lt;foaf:member rdf:resource='http://musicbrainz.org/artist/0ce1a4c2-ad1e-40d0-80da-d3396bc6518a'/&gt;
      &lt;foaf:member rdf:resource='http://musicbrainz.org/artist/1f52af22-0207-40ac-9a15-e5052bb670c2'/&gt;
      &lt;foaf:member rdf:resource='http://musicbrainz.org/artist/a94e530f-4e9f-40e6-b44b-ebec06f7900e'/&gt;
      &lt;foaf:member rdf:resource='http://musicbrainz.org/artist/7f347782-eb14-40c3-98e2-17b6e1bfe56c'/&gt;
      &lt;mo:wikipedia rdf:resource='http://en.wikipedia.org/wiki/U2_%28band%29'/&gt;
    &lt;/mo:MusicGroup&gt;
  &lt;/dc:creator&gt;
&lt;/mo:Track&gt;</pre>
   </div>
   
   <p>Example: The parameter is a URL that contains an MP3 file. In this case it 
      reads the ID3 tags from the MP3 file. See an output example at
      <a href="http://foafing-the-music.iua.upf.edu/RDFize/track?url=http://www.archive.org/download/bt2002-11-21.shnf/bt2002-11-21d1.shnf/bt2002-11-21d1t01_64kb.mp3">
      http://foafing-the-music.iua.upf.edu/RDFize/track?url=http://www.archive.org/download/bt2002-11-21.shnf/bt2002-11-21d1.shnf/bt2002-11-21d1t01_64kb.mp3</a> 
      (it might take a little while).
   </p>
   <div class="exampleInner" style="clear: both">
     <pre>
 &lt;mo:Track rdf:about='http://musicbrainz.org/track/7201c2ab-e368-4bd3-934f-5d936efffcdc'&gt;
  &lt;dc:creator&gt;
    &lt;mo:MusicGroup rdf:about='http://musicbrainz.org/artist/6b28ecf0-94e6-48bb-aa2a-5ede325b675b'&gt;
      &lt;foaf:name&gt;Blues Traveler&lt;/foaf:name&gt;
      &lt;mo:discogs rdf:resource='http://www.discogs.com/artist/Blues+Traveler'/&gt;
      &lt;foaf:homepage rdf:resource='http://www.bluestraveler.com/'/&gt;
      &lt;foaf:member rdf:resource='http://musicbrainz.org/artist/d73c9a5d-5d7d-47ec-b15a-a924a1a271c4'/&gt;
      &lt;mo:wikipedia rdf:resource='http://en.wikipedia.org/wiki/Blues_Traveler'/&gt;
      &lt;foaf:img rdf:resource='http://ec1.images-amazon.com/images/P/B000078JKC.01._SCMZZZZZZZ_.jpg'/&gt;
    &lt;/mo:MusicGroup&gt;
  &lt;/dc:creator&gt;
  &lt;dc:title&gt;Back in the Day&lt;/dc:title&gt;
  &lt;mo:puid rdf:resource='0a57a829-9d3c-eb35-37a8-d0364d1eae3a'/&gt;
  &lt;mo:puid rdf:resource='02039e1b-64bd-6862-2d27-3507726a8268'/&gt;
&lt;/mo:Track&gt;</pre>
   </div>

   <p>Example: Once the songs have been RDFized, we can ask <a href="http://last.fm">last.fm</a>
   for the latest tracks a user has been listening to, and then RDFize them. 
   <a href="http://foafing-the-music.iua.upf.edu/draft/RDFize/examples/lastfm_tracks.rdf">
   http://foafing-the-music.iua.upf.edu/draft/RDFize/examples/lastfm_tracks.rdf</a> is an example that 
   shows the latest tracks a user (RJ) has been listening to. 
   You can try it at <a href="http://foafing-the-music.iua.upf.edu/RDFize/lastfm_tracks?username=RJ">
   http://foafing-the-music.iua.upf.edu/RDFize/lastfm_tracks?username=RJ</a>
   </p>

    <!-- ======================================================================== -->
    <h3>
     <a name="news">2.3 Use Case: News</a>
    </h3>

    <h4 id="news-introduction">Introduction</h4>
    <p>More and more news is produced and consumed each day. News generally consists 
     of mainly textual stories, which are more and more often illustrated with 
     graphics, images and videos. News can be further processed by professional 
     (newspapers), directly accessible for web users through news agencies, or 
     automatically aggregated on the web, generally by search engine portal and not 
     without copyright problems.
    </p>
    <p>For easing the exchange of news, the <a href="http://www.iptc.org/pages/index.php">
     International Press Telecommunication Council (IPTC)</a> is currently 
     developping the NewsML G2 Architecture (NAR) whose goal is <em>to provide a 
     single generic model for exchanging all kinds of newsworthy information, thus 
     providing a framework for a future family of IPTC news exchange standards</em> 
     [<a href="#NewsML-G2">NewsML-G2</a>]. This family includes NewsML, SportsML, EventsML, ProgramGuideML and a 
     future WeatherML. All are XML-based languages used for describing not only the 
     news content (traditional metadata), but also their management and packaging, 
     or related to the exchange itself (transportation, routing).
    </p>
    <p>However, despite this general framework, interoperability problems can occur. 
     News is about the world, so its metadata might use specific controlled 
     vocabularies. For example, IPTC itself is developing the IPTC News Codes [<a href="#NewsCodes">NewsCodes</a>] 
     that currently contain 28 sets of controlled terms. These terms will be the 
     values of the metadata in the NewsML G2 Architecture. The news descriptions 
     often refer to other thesaurus and controlled vocabularies, that might come 
     from the industry (for example, XBRL [<a href="#XBRL">XBRL</a>] in the financial domain), and all are 
     represented using different formats. From the media point of view, the pictures 
     taken by the journalist come with their EXIF metadata [<a href="#Exif">EXIF</a>]. Some videos might be 
     described using the EBU format [<a href="#EBU">EBU</a>] or even with MPEG-7 [<a href="#MPEG-7">MPEG-7</a>].
    </p>
    <p>We illustrate these interoperability issues between domain vocabularies and 
     other multimedia standards in the financial news domain. For example, the 
     <a href="http://www.reuters.com/">Reuters Newswires</a> and the 
     <a href="http://www.djnewswires.com/">Dow Jones Newswires</a> provide categorical 
     metadata associated with news feeds. The particular vocabularies of category 
     codes, however, have been developed independently, leading to clear 
     interoperability issues. The general goal is to improve the search and the 
     presentation of news content in such an heterogeneous environment. We provide a 
     motivating example that highlight the issues discussed above and we present a 
     potential solution to this problem, which leverages Semantic Web technologies.
    </p>

    <h4 id="news-scenario">Motivating Example</h4>
    <p>XBRL (Extended Business Reporting Language) [<a href="#XBRL">XBRL</a>] is a standardized way of 
     enconding financial information of companies, and about the management 
     structure, location, number of employes, etc. of such entities. XBRL is 
     basically about "quantitative" information in the financial domain, and is 
     based on the periodic reports generated by the companies. But for many Business 
     Intelligence applications, there is also a need to consider "qualitative" 
     information, which is mostly delivered by news articles. The problem is 
     therefore how to optimally integrate information from the periodic reports and 
     the day to day information provided by specialized news agencies. Our goal is 
     to provide a platform that allows more semantics in automated ranking of 
     creditworthiness of companies. The financial news are playing an important role 
     since they provide "qualitative" information on companies, branches, trends, 
     countries, regions etc.
    </p>
    <p>There are quite a few news feeds services within the financial domain, 
     including the Dow Jones Newswire and Reuters. Both Reuters and Dow Jones 
     provides an XML based representation and have associated with each article 
     metadata with date, time, headline, full story, company ticker symbol, and 
     category codes.
    </p>

    <h5 id="news-example1">Example 1: NewsML 1 Format</h5>
     <p>We consider the news feeds similar to that published by <a href="http://www.reuters.com/">Reuters</a>, where 
      along with the text of the article, there is associated metadata in the form of 
      XML tags. The terms in these tags are associated with a controlled vocabulary 
      developed by Reuters and other industry bodies. Below is a sample news article 
      formatted in NewsML 1, which is similar to the structural format used by 
      Reuters. For exposition, the metadata tags associated with the article are 
      aligned with those used by Reurters.
     </p>
     <div class="exampleInner" style="clear: both"><pre>
&lt;?xml version="1.0" encoding="UTF-8"?&gt;
&lt;NewsML Duid="MTFH93022_2006-12-14_23-16-17_NewsML"&gt;
  &lt;Catalog Href="..."/&gt;
  &lt;NewsEnvelope&gt;
    &lt;DateAndTime&gt;20061214T231617+0000&lt;/DateAndTime&gt;
    &lt;NewsService FormalName="..."/&gt;
    &lt;NewsProduct FormalName="TXT"/&gt;
    &lt;Priority FormalName="3"/&gt;
  &lt;/NewsEnvelope&gt;
  &lt;NewsItem Duid="MTFH93022_2006-12-14_23-16-17_NEWSITEM"&gt;
    &lt;Identification&gt;
      &lt;NewsIdentifier&gt;
        &lt;ProviderId&gt;...&lt;/ProviderId&gt;
        &lt;DateId&gt;20061214&lt;/DateId&gt;
        &lt;NewsItemId&gt;MTFH93022_2006-12-14_23-16-17&lt;/NewsItemId&gt;
        &lt;RevisionId Update="N" PreviousRevision="0"&gt;1&lt;/RevisionId&gt;
        &lt;PublicIdentifier&gt;...&lt;/PublicIdentifier&gt;
      &lt;/NewsIdentifier&gt;
      &lt;DateLabel&gt;2006-12-14 23:16:17 GMT&lt;/DateLabel&gt;
    &lt;/Identification&gt;
    &lt;NewsManagement&gt;
      &lt;NewsItemType FormalName="News"/&gt;
      &lt;FirstCreated&gt;...&lt;/FirstCreated&gt;
      &lt;ThisRevisionCreated&gt;...&lt;/ThisRevisionCreated&gt;
      &lt;Status FormalName="Usable"/&gt;
      &lt;Urgency FormalName="3"/&gt;
    &lt;/NewsManagement&gt;
    &lt;NewsComponent EquivalentsList="no" Essential="no" Duid="MTFH92062_2002-09-23_09-29-03_T88093_MAIN_NC" xml:lang="en"&gt;
      &lt;TopicSet FormalName="HighImportance"&gt;  
        &lt;Topic Duid="t1"&gt;  
          &lt;TopicType FormalName="CategoryCode"/&gt; 
          &lt;FormalName Scheme="MediaCategory"&gt;OEC&lt;/FormalName&gt;  
          &lt;Description xml:lang="en"&gt;Economic news, EC, business/financial pages&lt;/Description&gt;  
        &lt;/Topic&gt;  
        &lt;Topic Duid="t2"&gt;  
          &lt;TopicType FormalName="Geography"/&gt;  
          &lt;FormalName Scheme="N2000"&gt;DE&lt;/FormalName&gt;  
          &lt;Description xml:lang="en"&gt;Germany&lt;/Description&gt;  
        &lt;/Topic&gt; 
      &lt;/TopicSet&gt;
      &lt;Role FormalName="Main"/&gt;
      &lt;AdministrativeMetadata&gt;
        &lt;FileName&gt;MTFH93022_2006-12-14_23-16-17.XML&lt;/FileName&gt;
        &lt;Provider&gt;
          &lt;Party FormalName="..."/&gt;
        &lt;/Provider&gt;
        &lt;Source&gt;
          &lt;Party FormalName="..."/&gt;
        &lt;/Source&gt;
        &lt;Property FormalName="SourceFeed" Value="IDS"/&gt;
        &lt;Property FormalName="IDSPublisher" Value="..."/&gt;
      &lt;/AdministrativeMetadata&gt;
      &lt;NewsComponent EquivalentsList="no" Essential="no" Duid="MTFH93022_2006-12-14_23-16-17" xml:lang="en"&gt;
        &lt;Role FormalName="Main Text"/&gt;
        &lt;NewsLines&gt;
          &lt;HeadLine&gt;Insurances get support&lt;/HeadLine&gt;
          &lt;ByLine/&gt;
          &lt;DateLine&gt;December 14, 2006&lt;/DateLine&gt;
          &lt;CreditLine&gt;...&lt;/CreditLine&gt;
          &lt;CopyrightLine&gt;...&lt;/CopyrightLine&gt;
          &lt;SlugLine&gt;...&lt;/SlugLine&gt;
          &lt;NewsLine&gt;
            &lt;NewsLineType FormalName="Caption"/&gt;
            &lt;NewsLineText&gt;Insurances get support&lt;/NewsLineText&gt;
          &lt;/NewsLine&gt;
        &lt;/NewsLines&gt;
        &lt;DescriptiveMetadata&gt;
          &lt;Language FormalName="en"/&gt;
          &lt;TopicOccurrence Importance="High" Topic="#t1"/&gt;
          &lt;TopicOccurrence Importance="High" Topic="#t2"/&gt;
        &lt;/DescriptiveMetadata&gt;
        &lt;ContentItem Duid="MTFH93022_2006-12-14_23-16-17"&gt;
          &lt;MediaType FormalName="Text"/&gt;
          &lt;Format FormalName="XHTML"/&gt;
          &lt;Characteristics&gt;
            &lt;Property FormalName="ContentID" Value="urn:...20061214:MTFH93022_2006-12-14_23-16-17_T88093_TXT:1"/&gt;
            ...
          &lt;/Characteristics&gt;
          &lt;DataContent&gt;
            &lt;html xmlns="http://www.w3.org/1999/xhtml"&gt;
              &lt;head&gt;
                &lt;title&gt;Insurances get support&lt;/title&gt;
              &lt;/head&gt;
              &lt;body&gt;
                &lt;h1&gt;The Senate of Germany wants to constraint the participation of clients to the hidden reserves&lt;/h1&gt;
                &lt;p&gt;
                 D&#xc3;&#x9c;SSELDORF The German Senate supports the point of view of insurance companies in a central point of the new law
                 defining insurance contracts, foreseen for 2008. In a statement, the Senators show disagreements with the proposal
                 of  the Federal Government, who was in favor of including investment bonds in the hidden reserves, which in the 
                 next future should be accessible to the clients of the insurance companies.
                 ...
                &lt;/p&gt;
              &lt;/body&gt;
            &lt;/html&gt;
          &lt;/DataContent&gt;
        &lt;/ContentItem&gt;
      &lt;/NewsComponent&gt;
    &lt;/NewsComponent&gt;
  &lt;/NewsItem&gt;
&lt;/NewsML&gt;</pre>
     </div>

     <h5 id="news-example2">Example 2: NewsML G2 Format</h5>
     <p>If we consider the same data, but expressed in NewsML G2:
     </p>
     <div class="exampleInner" style="clear: both">
      <pre>&lt;?xml version="1.0" encoding="UTF-8"?&gt;
&lt;newsMessage xmlns="http://iptc.org/std/newsml/2006-05-01/" xmlns:xhtml="http://www.w3.org/1999/xhtml"&gt;
  &lt;header&gt;
    &lt;date&gt;2006-12-14T23:16:17Z&lt;/date&gt;
    &lt;transmitId&gt;696&lt;/transmitId&gt;
    &lt;priority&gt;3&lt;/priority&gt;
    &lt;channel&gt;ANA&lt;/channel&gt;
  &lt;/header&gt;
  &lt;itemSet&gt;
    &lt;newsItem guid="urn:newsml:afp.com:20060720:TX-SGE-SNK66" schema="0.7" version="1"&gt;
      &lt;catalogRef href="http://www.afp.com/newsml2/catalog-2006-01-01.xml"/&gt;
      &lt;itemMeta&gt;
        &lt;contentClass code="ccls:text"/&gt;
        &lt;provider literal="Handelsblatt"/&gt;
        &lt;itemCreated&gt;2006-07-20T23:16:17Z&lt;/itemCreated&gt;
        &lt;pubStatus code="stat:usable"/&gt;
        &lt;service code="srv:Archives"/&gt;
      &lt;/itemMeta&gt;
      &lt;contentMeta&gt;
        &lt;contentCreated&gt;2006-07-20T23:16:17Z&lt;/contentCreated&gt;
        &lt;creator/&gt;
        &lt;language literal="en"/&gt;
        &lt;subject code="cat:04006002" type="ctyp:category"/&gt;        #cat:04006002= banking
        &lt;subject code="cat:04006006" type="ctyp:category"/&gt;        #cat:04006006= insurance
        &lt;slugline separator="-"&gt;Insurances get support&lt;/slugline&gt;
        &lt;headline&gt;The Senate of Germany wants to constraint the participation of clients to the hidden reserves&lt;/headline&gt;
      &lt;/contentMeta&gt;
      &lt;contentSet&gt;
        &lt;inlineXML type="text/plain"&gt;
&lt;html xmlns="http://www.w3.org/1999/xhtml"&gt;
  &lt;head&gt;
    &lt;title&gt;Insurances get support&lt;/title&gt;
  &lt;/head&gt;
  &lt;body&gt;
    &lt;h1&gt;The Senate of Germany wants to constraint the participation of clients to the hidden reserves&lt;/h1&gt;
    &lt;p&gt;
    D&#xc3;&#x9c;SSELDORF The German Senate supports the point of view of insurance companies in a central point of the new law defining 
    insurance contracts, foreseen for 2008. In a statement, the Senators show disagreements with the proposal of the Federal 
    Government, who was in favor of including investment bonds in the hidden reserves, which in the next future should be accessible 
    to the clients of the insurance companies.
    ...
    &lt;/p&gt;
  &lt;/body&gt;
&lt;/html&gt;
        &lt;/inlineXML&gt;
      &lt;/contentSet&gt;
    &lt;/newsItem&gt;
  &lt;/itemSet&gt;
&lt;/newsMessage&gt;</pre>
     </div>

     <h5 id="news-example3">Example 3: German Broadcaster Format</h5>
     <p>The terms in the tags displayed just above are associated with a controlled 
      vocabulary developed by Reuters. If we consider the internal XML encoding that 
      has been proposed provisionally by a running European project (the <a href="http://www.musing.eu">MUSING 
      project</a>) for the encoding of similar articles in German 
      Newspapers (mapping the HTML tags of the online articles into XML and adding 
      others), we have the following:
     </p>

     <div class="exampleInner" style="clear: both"><pre>
  &lt;ID&gt;1091484&lt;/ID&gt;                 # Internal encoding
  &lt;SOURCE&gt;Handelsblatt&lt;/SOURCE&gt;    # Name of the newspaper we get the information from
  &lt;DATE&gt;14.12.2006&lt;/DATE&gt;          # Date of publication
  &lt;NUMBER&gt;242&lt;/NUMBER&gt;             # Numbering of the publication
  &lt;PAGE&gt;27&lt;/PAGE&gt;                  # Page number in the publication
  &lt;LENGTH&gt;111&lt;/LENGTH&gt;             # The number of lines in the main article

  &lt;ACTIVITY_FIELD&gt;Banking_Insurance&lt;/ACTIVITY_FIELD&gt;   # corresponding to the financial domain reported in the article
  &lt;TITLE&gt;Insurances get support&lt;/TITLE&gt;
  &lt;SUBTITLE&gt;The Senate of Germany wants to constraint the participation of clients to the hidden reserves&lt;/SUBTITLE&gt;
  &lt;ABSTRACT&gt;&lt;/ABSTRACT&gt;
  &lt;AUTHORS&gt;Lansch, Rita&lt;/AUTHORS&gt;
  &lt;LOCATION&gt;Federal Republic of Germany&lt;/LOCATION&gt;
  &lt;KEYWORDS&gt;Bank supervision, Money and Stock exchange, Bank&lt;/KEYWORDS&gt; 
  &lt;PROPERNAMES&gt;Meister, Edgar Remsperger, Hermann Reckers, Hans Fabritius, Hans Georg Zeitler, Franz-Christoph&lt;/PROPERNAMES&gt; 
  &lt;ORGANISATIONS&gt;Bundesanstalt f&#xc3;&#xbc;r Finanzdienstleistungsaufsicht BAFin&lt;/ORGANISATIONS&gt;
  &lt;TEXT&gt;D&#xc3;&#x9c;SSELDORF The German Senate supports the point of view of insurance companies in a central point of the new law 
  defining insurance contracts, foreseen for 2008. In a statement, the Senators show disagreements with the proposal of the 
  Federal Government, who was in favor of including investment bonds in the hidden reserves, which in the next future should 
  be accessible to the clients of the insurance companies....&lt;/TEXT&gt;
</pre>
     </div>
     
     <h5 id="news-example4">Example 4: XBRL Format</h5>
     <p>Structured data and documents such as Profit &amp; Loss tables can finally be 
      mapped onto existing taxonomies, like XBRL, which is an emerging standard for 
      Business Reporting.
     </p>
     <p>XBRL definition in Wikipedia: "XBRL is an emerging XML-based standard to 
      define and exchange business and financial performance information. The 
      standard is governed by a not-for-profit international consortium 
      <a href="http://www.xbrl.org">XBRL International Incorporated</a>
      of approximately 450 organizations, including regulators, government agencies, 
      infomediaries and software vendors. XBRL is a standard way to communicate 
      business and financial performance data. These communications are defined by 
      metadata set in taxonomies. Taxonomies capture the definition of individual 
      reporting elements as well as the relationships between elements within a 
      taxonomy and in other taxonomies.
     </p>

     <p>The relations between elements supported, for the time being, (at least for 
      the German Accounting Principles expressed in the corresponding XBRL taxonomy, 
      see <a href="http://www.xbrl.de/">http://www.xbrl.de/</a>) are:
     </p>
     <ul>
      <li>
       child-parent
      </li>
      <li>
       parent-child
      </li>
      <li>
       dimension-element
      </li>
      <li>
       element-dimension
      </li>
     </ul>
     <p>In fact the child-parent/parent-child relation haves to be understood as 
      part-of relations within finanical reporting documents rather than as sub-class 
      relations, as we noticed in an attempt to formlize XBRL in OWL, in the context 
      of the European MUSING R&amp;D project (<a href="http://www.musing.eu/">http://www.musing.eu/</a>).
     </p>
     <p>The table below shows how a balance sheet looks like:
     </p>
     <div>
      <table border="2" id="Table1">
       <tbody>
        <tr>
         <td><strong>structured P&amp;L</strong>
         </td>
         <td><strong>2002 EUR</strong>
         </td>
         <td><strong>2002 EUR</strong>
         </td>
         <td><strong>2002 EUR</strong>
         </td>
        </tr>
        <tr>
         <td>
          Sales
         </td>
         <td>850.000,00
         </td>
         <td>800.000,00
         </td>
         <td>300.000,00
         </td>
        </tr>
        <tr>
         <td>
          Changes in stock
         </td>
         <td>171.000,00
         </td>
         <td>104.000,00
         </td>
         <td>83.000,00
         </td>
        </tr>
        <tr>
         <td>
          Own work capitalized
         </td>
         <td>0,00
         </td>
         <td>0,00
         </td>
         <td>0,00
         </td>
        </tr>
        <tr>
         <td>
          Total output
         </td>
         <td>1.021.000,00
         </td>
         <td>904.000,00
         </td>
         <td>383.000,00
         </td>
        </tr>
        <tr>
         <td>
          ...
         </td>
         <td>
         </td>
         <td>
         </td>
         <td>
         </td>
        </tr>
        <tr>
         <td>
          Net income/net loss for the year
         </td>
         <td>139.000,00
         </td>
         <td>180.000,00
         </td>
         <td>-154.000,00
         </td>
        </tr>
        <tr>
         <td>
         </td>
         <td><strong>2002</strong>
         </td>
         <td><strong>2001</strong>
         </td>
         <td><strong>2000</strong>
         </td>
        </tr>
        <tr>
         <td>
          Number of Employees
         </td>
         <td>27
         </td>
         <td>25
         </td>
         <td>23
         </td>
        </tr>
        <tr>
         <td>
          ....
         </td>
         <td>
         </td>
         <td>
         </td>
         <td>
         </td>
        </tr>
       </tbody>
      </table>
     </div>
     <p>There is a lot of variations in both the way the information can be displayed 
      (number of columns, use of fonts, etc.) but also in the terminology used: the 
      financial terms in the leftmost column are not normalized at all. Also the 
      figures are not normalized (clearly, the company has more than just "27" 
      employees, but it is not indicated in the table if we deal with 27000 
      employess). This makes this kind of information unable to be used by semantic 
      applications. XBRL is a very important step in the normalization of such data, 
      as can be seen in the following example displaying the XBRL encoding of the 
      kind of data that was presented just above in the table:
     </p>

     <div class="exampleInner" style="clear: both">
      <pre>&lt;group xsi:schemaLocation="http://www.xbrl.org/german/ap/ci/2002-02-15 german_ap.xsd"&gt;
   &lt;numericContext id="c0" precision="8" cwa="false"&gt; 
      &lt;entity&gt;
         &lt;identifier scheme="urn:datev:www.datev.de/zmsd"&gt;11115,129472/12346&lt;/identifier&gt;
      &lt;/entity&gt; 
      &lt;period&gt;
         &lt;startDate&gt;2002-01-01&lt;/startDate&gt; 
         &lt;endDate&gt;2002-12-31&lt;/endDate&gt;
      &lt;/period&gt; 
      &lt;unit&gt;
         &lt;measure&gt;ISO4217:EUR&lt;/measure&gt;
      &lt;/unit&gt; 
   &lt;/numericContext&gt; 
   &lt;numericContext id="c1" precision="8" cwa="false"&gt; 
      &lt;entity&gt;
         &lt;identifier scheme="urn:datev:www.datev.de/zmsd"&gt;11115,129472/12346&lt;/identifier&gt;
      &lt;/entity&gt; 
      &lt;period&gt;
         &lt;startDate&gt;2001-01-01&lt;/startDate&gt; 
         &lt;endDate&gt;2001-12-31&lt;/endDate&gt;
      &lt;/period&gt; 
      &lt;unit&gt;
         &lt;measure&gt;ISO4217:EUR&lt;/measure&gt;
      &lt;/unit&gt;
   &lt;/numericContext&gt; 
   &lt;numericContext id="c2" precision="8" cwa="false"&gt; 
      &lt;entity&gt;
         &lt;identifier scheme="urn:datev:www.datev.de/zmsd"&gt;11115,129472/12346&lt;/identifier&gt;
      &lt;/entity&gt; 
      &lt;period&gt;
         &lt;startDate&gt;2000-01-01&lt;/startDate&gt; 
         &lt;endDate&gt;2000-12-31&lt;/endDate&gt;
      &lt;/period&gt; 
      &lt;unit&gt;
         &lt;measure&gt;ISO4217:EUR&lt;/measure&gt;
      &lt;/unit&gt; 
   &lt;/numericContext&gt; 
   &lt;t:bs.ass numericContext="c2"&gt;1954000&lt;/t:bs.ass&gt; 
   &lt;t:bs.ass.accountingConvenience numericContext="c0"&gt;40000&lt;/t:bs.ass.accountingConvenience&gt; 
   &lt;t:bs.ass.accountingConvenience numericContext="c1"&gt;70000&lt;/t:bs.ass.accountingConvenience&gt; 
   &lt;t:bs.ass.accountingConvenience numericContext="c2"&gt;0&lt;/t:bs.ass.accountingConvenience&gt; 
   &lt;t:bs.ass.accountingConvenience.changeDem2Eur numericContext="c0"&gt;0&lt;/t:bs.ass.accountingConvenience.changeDem2Eur&gt;     
   &lt;t:bs.ass.accountingConvenience.changeDem2Eur numericContext="c1"&gt;20000&lt;/t:bs.ass.accountingConvenience.changeDem2Eur&gt; 
   &lt;t:bs.ass.accountingConvenience.changeDem2Eur numericContext="c2"&gt;0&lt;/t:bs.ass.accountingConvenience.changeDem2Eur&gt; 
   &lt;t:bs.ass.accountingConvenience.startUpCost numericContext="c0"&gt;40000&lt;/t:bs.ass.accountingConvenience.startUpCost&gt; 
   &lt;t:bs.ass.accountingConvenience.startUpCost numericContext="c1"&gt;50000&lt;/t:bs.ass.accountingConvenience.startUpCost&gt; 
   &lt;t:bs.ass.accountingConvenience.startUpCost numericContext="c2"&gt;0&lt;/t:bs.ass.accountingConvenience.startUpCost&gt; 
   &lt;t:bs.ass.currAss numericContext="c0"&gt;571500&lt;/t:bs.ass.currAss&gt; 
   &lt;t:bs.ass.currAss numericContext="c1"&gt;558000&lt;/t:bs.ass.currAss&gt; 
   &lt;t:bs.ass.currAss numericContext="c2"&gt;394000&lt;/t:bs.ass.currAss&gt;
&lt;/group&gt;</pre>
     </div>
     <p>In the XBRL example shown just above, one can see the normalization of the 
      periods for which the reporting is valid, and for the currency used in the 
      report. The annotation of the financial values of the financial items is then 
      proposed on the base of a XBRL tag (language independent) in the context of the 
      uniquely identified period (the "c0", "c1" etc), and with the encoded currency.
     </p>
     <p>The XBRL representation is marking a real progress compared to the 
      "classical" way of displaying financial information. And as such XBRL allows 
      for some semantics, describing for example various types of relations. The need 
      for more semantics is mainly driven by applications requiring merging of the 
      quantitative information encoded in XBRL with other kind of information, which 
      is crucial in Business Intelligence scenarios, for example merging balance 
      sheet information with information coming from newswires or with information in 
      related domain, like politics. Therefore some initiatives started looking at 
      representing information encoded in XBRL within OWL, as the basic ontology 
      language representation in the Semantic Web community [<a href="#Declerck">Declerck</a>], [<a href="#Lara">Lara</a>].
     </p>

     <h4 id="news-solution">Potential Solution: Converting Various Vocabularies into RDF</h4>
     <p>In this section, we discuss a potential solution to the problems highlighted 
      in this document. We propose utilizing Semantic Web technologies for the 
      purpose of aligning these standards and controlled vocabularies. Specifically, 
      we discuss adding an RDF/OWL layer on top of these standards and vocabularies 
      for the purpose of data integration and reuse. The following sections discuss 
      this approach in more detail.
     </p>

     <h5 id="news-XBRL">XBRL in the Semantic Web</h5>
     <p>We sketch how we convert XBRL to OWL. The XBRL OWL base taxonomy was manually 
      developed using the OWL plugin of the Protege knowledge base editor [<a href="#Knublauch">Knublauch</a>]. The 
      version of XBRL we used together with the Accounting Principles for German 
      consists of 2,414 concepts, 34 properties, and 4,780 instances. Overall, this 
      translates into 24,395 unique RDF triples. The basic idea during our export was 
      that even though we are developing an XBRL taxonomy in OWL using Protege, the 
      information that is stored on disk is still RDF on the syntactic level. We were 
      thus interested in RDF data base systems which make sense of the semantics of 
      OWL and RDFS constructs such as rdfs:subClassOf or owl:equivalentClass. We have 
      been experimenting with the Sesame open-source middleware framework for storing 
      and retrieving RDF data [<a href="#Broekstra">Broekstra</a>].
     </p>
     <p>Sesame partially supports the semantics of RDFS and OWL constructs via 
      entailment rules that compute "missing" RDF triples (the deductive closure) in 
      a forward-chaining style at compile time. Since sets of RDF statements 
      represent RDF graphs, querying information in an RDF framework means to specify 
      path expressions. Sesame comes with a very powerful query language, SeRQL, 
      which includes (i) generalised path expressions, (ii) a restricted form of 
      disjunction through optional matching, (iii) existential quantifiation over 
      predicates, and (iv) Boolean constraints. From an RDF point of view, additional 
      62,598 triples were generated through Sesame's (incomplete) forward chaining 
      inference mechanism.
     </p>
     <p>For proof of concept, we looked at the freely available financial reporting 
      taxonomies(<a href="http://www.xbrl.org/FRTaxonomies/">http://www.xbrl.org/FRTaxonomies/</a>) 
      and took the final German AP Commercial and Industrial (German Accounting 
      Principles) taxonomy (February 15, 2002; <a href="http://www.xbrl-deutschland.de/xe">
      http://www.xbrl-deutschland.de/xe</a> news2.htm), acknowledged by XBRL 
      International. The taxonomy can be obtained as a packed zip file from <a href="http://www.xbrl-deutschland.de/germanap.zip">
      http://www.xbrl-deutschland.de/germanap.zip</a>.
     </p>
     <p>xbrl-instance.xsd specifies the XBRL base taxonomy using XML Schema. The file 
      makes use of XML schema datatypes, such as xsd:string or xsd:date, but also 
      defines simple types (simpleType), complex types (complexType), elements 
      (element), and attributes (attribute). Element and attribute declarations are 
      used to restrict the usage of elements and attributes in XBRL XML documents. 
      Since OWL only knows the distinction between classes and properties, the 
      correpondences between XBRL and OWL description primitives is not a one-to-one 
      mapping:
     </p>
     <p>However, OWL allows to characterize properties more precisely than just 
      having only a domain and a range. We can mark a property as functional (instead 
      of being relational, the default case), meaning that it takes at most one 
      value. This clearly means that a property must not have a value for each 
      instance of a class on which it is defined. Thus a functional property is in 
      fact a partial (and must not necessarily be a total) function. Exactly the 
      distinction functional vs. relational is represented by the attribute vs. 
      element distinction, since multiple elements are allowed within a surrounding 
      context. However, at most one attribute-value combination for each attribute 
      name is allowed within an element:
     </p>
     <div>
      <table border="2" ID="Table2">
       <tbody>
        <tr>
         <td><strong>XBRL</strong></td>
         <td><strong>OWL</strong></td>
        </tr>
        <tr>
         <td>simple type</td>
         <td>class</td>
        </tr>
        <tr>
         <td>complex type</td>
         <td>class</td>
        </tr>
        <tr>
         <td>attribute</td>
         <td>functional property</td>
        </tr>
        <tr>
         <td>element</td>
         <td>relational property</td>
        </tr>
       </tbody>
      </table>
     </div>
     <p>Simple and complex types differs from one another in that simple types are 
      essentially defined as extensions of the basic XML Schema datatypes, whereas 
      complex types are XBRL specifications that do not build upon XSD types, but 
      instead introduce their own element and attribute descriptions. Here are simple 
      type specifications found in the base terminology of XBRL, located in the file 
      xbrl-instance.xsd:
     </p>
     <p>Since OWL only claims that "As a minimum, tools must support datatype 
      reasoning for the XML Schema datatypes xsd:string and xsd:integer." [<a href="#OWL">OWL</a>, p. 30] 
      and because "It is not illegal, although not recommended, for applications to 
      define their own datatypes ..." [<a href="#OWL">OWL</a>, p. 29], we have decided to implement a 
      workaround that represents all the necessary XML Schema datatypes used in XBRL. 
      This was done by having a wrapper type for each simple XML Schema type. For 
      instance, "monetary" is a simple subtype of the wrapper type "decimal": 
      <tt>&lt;restriction base="decimal"/&gt;</tt>. Below we show the first lines of the 
      actual OWL version of XBRL we have implemented:
     </p>
     <div class="exampleInner" style="clear: both">
       <pre>
&lt;?xml version="1.0"?&gt;
&lt;rdf:RDF xmlns="http://xbrl.dfki.de/main.owl#" 
         xmlns:protege="http://protege.stanford.edu/plugins/owl/protege#"
         xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
         xmlns:xsd="http://www.w3.org/2001/XMLSchema#"
         xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
         xmlns:owl="http://www.w3.org/2002/07/owl#"
         xml:base="http://xbrl.dfki.de/main.owl"&gt;
  &lt;owl:Ontology rdf:about=""/&gt;
  &lt;owl:Class rdf:ID="bs.ass.fixAss.tan.machinery.installations"&gt;
    &lt;rdfs:subClassOf&gt;
      &lt;owl:Class rdf:ID="Locator"/&gt;
    &lt;/rdfs:subClassOf&gt;
  &lt;/owl:Class&gt;
  &lt;owl:Class rdf:ID="nt.ass.fixAss.fin.loansToParticip.net.addition"&gt;
    &lt;rdfs:subClassOf&gt;
      &lt;owl:Class rdf:about="#Locator"/&gt;
    &lt;/rdfs:subClassOf&gt;
  &lt;/owl:Class&gt;
  &lt;owl:Class rdf:ID="nt.ass.fixAss.fin.loansToSharehold.net.beginOfPeriod.endOfPrevPeriod"&gt;
    &lt;rdfs:subClassOf&gt;
      &lt;owl:Class rdf:about="#Locator"/&gt;
    &lt;/rdfs:subClassOf&gt;
  &lt;/owl:Class&gt;
  &lt;owl:Class rdf:ID="nt.ass.fixAss.fin.gross.revaluation.comment"&gt;
    &lt;rdfs:subClassOf&gt;
      &lt;owl:Class rdf:about="#Locator"/&gt;
    &lt;/rdfs:subClassOf&gt;
  &lt;/owl:Class&gt;
  &lt;owl:Class rdf:ID="nt.ass.fixAss.fin.securities.gross.beginOfPeriod.otherDiff"&gt;
    &lt;rdfs:subClassOf&gt;
      &lt;owl:Class rdf:about="#Locator"/&gt;
    &lt;/rdfs:subClassOf&gt;
  &lt;/owl:Class&gt;
   ...
&lt;/owl:Ontology&gt;
&lt;/rdf:RDF&gt;</pre>
     </div>
     <p>The German Accounting Principles taxonomy consists of 2,387 concepts, plus 27 
      concepts from the base taxonomy for XBRL. 34 properties were defined and 4,780 
      instance fnally generated.
     </p>
     <p>Besides the ontologization of XBRL, we would propose to build an ontology on 
      the top of the taxonomic organization of NACE codes. Then we need a clear 
      ontological representation of the time units/information relevant in the 
      domain. And last but not least, we would also use all the 
      classification/categorization information of NewsML/IPTC to use more accurate 
      semantic Metadata for the encoding of the (financial) news articles.
     </p>

     <h5 id="news-exif">EXIF in the Semantic Web</h5>
     <p>One of today's commonly used image format and metadata standard is the 
      Exchangeable Image File Format [<a href="#Exif">EXIF</a>]. This file format provides a standard 
      specification for storing metadata regarding image. Metadata elements 
      pertaining to the image are stored in the image file header and are marked with 
      unique tags, which serves as an element identifying.
     </p>
     <p>As we note in this document, one potentional way to integrate EXIF metadata 
      with additinoal news/multimedia metadata formats is to add an RDF layer on top 
      of the metadata standards. Recently there has been efforts to encode EXIF 
      metadata in such Semantic Web standards, which we briefly detail below. We note 
      that both of these ontologies are semantically very similar, thus this issue is 
      not addressed here. Essentially both are a straightforward encodings of the 
      EXIF metadata tags for images. There are some syntactic differences, 
      but again they are quite similar; they primarily differ in their naming 
      conventions utilized.
     </p>

     <p>The <a href="http://www.kanzaki.com/test/exif2rdf">Kanzaki EXIF RDF Schema</a> provides an encoding of the basic EXIF 
      metadata tags in RDFS. Essentially, these are the tags defined from Section 4.6 
      of [<a href="#Exif">EXIF</a>]. We also note here that relevant domains and ranges are utilized as 
      well. It additionally provides an EXIF conversion service, EXIF-to-RDF, 
      which extracts EXIF metadata from images and automatically maps it to 
      the RDF encoding. In particular the service takes a URL to an EXIF image and 
      extracts the embedded EXIF metadata. The service then converts this metadata to 
      the RDF schema and returns this to the user.</p>

     <p>The <a href="http://www.nwalsh.com/java/jpegrdf/">Norm Walsh EXIF RDF Schema</a> provides another encoding of the basic 
      EXIF metadata tags in RDFS. Again, these are the tags defined from Section 4.6 
      of [<a href="#Exif">EXIF</a>]. It additionally provides JPEGRDF, which is a Java application that 
      provides an API to read and manipulate EXIF meatadata stored in JPEG images. 
      Currently, JPEGRDF can can extract, query, and augment the EXIF/RDF data stored 
      in the file headers. In particular, we note that the API can be used to convert 
      existing EXIF metadata in file headers to the schema. The 
      resulting RDF can then be stored in the image file header, etc. (Note here that 
      the API's functionality greatly extends that which was briefly presented here).
     </p>

     <h5 id="news-conclusion">Putting All That Together</h5>
     <p>Some text showing how this qualitative and quantitative information benefits 
      to interoperate ...</p>

     <!-- ======================================================================== -->

     <h3>
      <a name="tagging">2.4 Use Case: Tagging</a>
     </h3>

     <h4 id="tagging-introduction">Introduction</h4>
     <p>
      Tags are what may be the simplest form of annotation: simple user-provided 
      keywords that are assigned to resources, in order to support subsequent 
      retrieval. In itself, this idea is not particularly new or revolutionary: 
      keyword-based retrieval has been around for a while. In contrast to the formal 
      semantics provided by the Semantic Web standards, tags have no semantic 
      relations whatsoever, including a lack of hierarchy; tags are just flat 
      collections of keywords.<br/>
      There are however new dimensions that have boosted the popularity of this 
      approach and given a new perspective on an old theme: low-cost applicability 
      and collaborative tagging.
     </p>
     <p>
      Tagging lowers the barrier of metadata annotation, since it requires minimal 
      effort on behalf of annotators: there are no special tools or complex interface 
      that the user needs to get familiar with, and no deep understanding of logic 
      principles or formal semantics required &#xe2;&#x80;&#x93; just some standard technical 
      expertise. Tagging seems to work in a way that is intuitive to most people, as 
      demonstrated by its widespread adoption, as well as by certain studies 
      conducted on the field [<a href="#Trant">Trant</a>]. Thus, it helps bridging the 'semantic gap' between 
      content creators and content consumers, by offering 'alternative points of 
      access' to document collections.
     </p>
     <p>
      The main idea behind collaborative tagging is simple: collaborative tagging 
      platforms (or, alternatively, distributed classification systems - DCSs [<a href="#Mejias">Mejias</a>]) 
      provide the technical means, usually via some sort of web-based interface, that 
      support users in tagging resources. What is the important aspect of this is 
      that they aggregate collections of tags that an individual uses, or his tag 
      vocabulary, called a personomy [<a href="#Hotho">Hotho</a>], into what has been termed a folksonomy: a 
      collection of all personomies [<a href="#Mathes">Mathes</a>, <a href="#Smith">Smith</a>].
     </p>
     <p>Some of the most popular collaborative tagging systems are Delicious 
      (bookmarks), Flickr (images), Last.fm (music), YouTube (video), Connotea 
      (bibliographic information), steve.museum (museum items) and Technorati 
      (blogging). Using these platforms is free, although in some cases users can opt 
      for more advanced features by getting an upgraded account, for which they have 
      to pay. The most prominent among them are Delicious and Flickr, for which some 
      quantitative user studies are available [<a href="#HitWise">HitWise</a>, 
      <a href="#NetRatings">NetRatings</a>]. These user studies document a 
      phenomenal growth, that indicates that in real-life tagging is a very viable 
      solution for annotating any type of resource.
     </p>

     <h4 id="tagging-scenario">Motivating Scenario</h4>
     <p>
      Let us view some of the current limitations of tag-based annotation, by 
      examining a motivating example:
     </p>
     <p>
      Let's suppose that user Mary has an account on platform S1, that specializes in 
      images. Mary has been using S1 for a while, so she has progressively built a 
      large image collection, as well as a rich vocabulary of tags (personomy).
     </p>
     <p>
      Another user, Sylvia, who is Mary's friend, is using a different platform, S2, 
      to annotate her images. At some point, Mary and Sylvia attended the same event, 
      and each one took some pictures with her own camera. As each user has her 
      reasons for choosing a preferred platform, none of them would like to change. 
      They would like however to be able to link to each other's annotated pictures, 
      where applicable: it can be expected that since the pictures were taken at the 
      same time and place, some of them may be annotated in similar way (same tags), 
      even by different annotators. So they may (within the boundaries of word 
      ambiguity) be about the same topic.
     </p>
     <p>In the course of time Mary also becomes interested in video and starts 
      shooting some of her own. As her personal video collection begins to grow, she 
      decides to start using another collaborative tagging system, S3, that 
      specializes in video, in order to better organise it. Since she already has a 
      rich personomy built in S1, she would naturally like to reuse it in S3, to the 
      extent possible: while some of the tags may not be appropriate, as they may 
      represent one-off ('29-08-06') or photography-specific ('CameraXYZ') use, 
      others might as well be reused across modalities/domains, in case they 
      represent high-level concepts ('holidays'). So if Mary has both video and 
      photographic material of some event, and since she has already created a 
      personomy on S1, she would naturally like to be able to reuse it (partially, 
      perhaps) on S2 as well.
     </p>
     
     <h4 id="tagging-issues">Issues</h4>
     <p>
      The above scenario demonstrates limitations of tag-based systems with respect 
      to personomy reuse:
     </p>
     <ul>
      <li>
       A personomy maintained at one platform cannot easily be reused for a tag-based 
       retrieval on another tagging platform.
      </li>
      <li>
       A personomy maintained at one platform cannot easily be reused to organize 
       further media or resources on another tagging platform.
      </li>
     </ul>
     <p>
      As media resides not only on Internet platforms but is most likely maintained 
      on a local computer at first, local organizational structures can also not 
      easily be transferred to a tagging platform. The opposite holds as well, a 
      personomy maintained on a tagging platform cannot easily be reused on a desktop 
      computer.
     </p>
     <p>
      Personomy reuse is currently not easily possible as each platform uses ad-hoc 
      solutions and only provides tag navigation within its own boundaries: there is 
      no standardization that regulates how tags and relations between tags, users, 
      and resources are represented. Due to that lack of standardization there are 
      further technical issues that become visible through the application 
      programming interfaces provided by some tagging platforms:
     </p>
     <ul>
      <li>
       Some platforms prohibit tags containing space characters while other allow such 
       tags
      </li>
      <li>
       Different platforms provide different functionality for organizing tags 
       themselves, e.g. some platforms allow to summarize tags in tag-bundles
      </li>
     </ul>

     <h4 id="tagging-solution">Possible Solutions</h4>
     <p>
      When it comes to interoperability, standards-based solutions have repeatedly 
      proven successful in enabling to bridge different systems. This could also be 
      the case here, as a standard for expressing personomies and folksonomies would 
      enable interoperability across platforms. On the other hand, use of a standard 
      should not enforce changes in the way tags are handled internally by each 
      system - it simply aims to function as a bridge between different systems. The 
      question is then, what standard?
     </p>
     <p>
      We may be able to answer this question if we consider a personomy as a concept 
      scheme: tags used by an individual express his or her expertise, interests and 
      vocabulary, thus constituting the individual's own concept scheme. A recent W3C 
      standard that has been designed specifically to express the basic structure and 
      content of concept schemes is SKOS Core [<a href="#SKOS">SKOS</a>]. The SKOS Core Vocabulary is an 
      application of the Resource Description Framework (RDF), that can be used to 
      express a concept scheme as an RDF graph. Using RDF allows data to be linked to 
      and/or merged with other RDF data by semantic web applications.
     </p>
     <p>
      Expressing personomies and folksonomies using SKOS is a good match for 
      promoting a standard representation for tags, as well as integrating tag 
      representation with Semantic Web standards: not only does it enable expression 
      of personomies in a standard format that fits semantically, but also allows 
      mixing personomies with existing Semantic Web ontologies. There is already a 
      publicly available SKOS-based tagging ontology that can be used to build on 
      [<a href="#Newman">Newman</a>], as well as some existing efforts to induce an ontology from collaborative 
      tagging platforms [<a href="#Schmitz">Schmitz</a>].
     </p>
     <p>Ideally, we would expect existing collaborative tagging platform to build on 
      a standard representation for tags in order to enable interoperability and 
      offer this as a service to their users. In practice however , even if such a 
      representation was eventually adopted as a standard, our expectation is that 
      there will be both technical and political reasons that could possibly hinder 
      its adoption. A different strategy that may be able to deal with this issue 
      then would be to implement this as a separate service that will integrate 
      disparate collaborative tagging platforms based on such an emergind standard 
      for tag representation, in the spirit of Web2.0 mashups. This service could 
      either be provided by a 3rd party, or even be self-hosted by individual users, 
      in the spirit of [<a href="#Koivunen">Koivunen</a>, <a href="#Segawa">Segawa</a>]</p>

     <!-- ======================================================================== -->
     <h3>
      <a name="semanticRetrieval">2.5 Use Case : Semantic Media Analysis for Intelligent Retrieval</a>
     </h3>

     <h4 id="retrieval-introduction">Introduction</h4>
     <p>Semantic Media Analysis seen from a multimedia retrieval perspective is 
      equivalent to the automatic creation of semantic indices and annotations based 
      on multimedia and domain ontologies to enable intelligent human-like multimedia 
      retrieval purposes. An efficient multimedia retrieval system [<a href="#Naphade">Naphade</a>], must:
     </p>
     <ol type="i">
      <li>
       Be able to handle the semantics of the query,
      </li>
      <li>
       Unify multiple modalities in a homogeneous frameworkb and
      </li>
      <li>
       Abstract the relationship between low level media features and high level 
       semantic concepts to allow the user to query in terms of these concepts rather 
       than in terms of examples, i.e. introduction the notion of ontologies.
      </li>
     </ol>
     <p>
      This Use Case aims to pinpoint problems that arise during the effort for an 
      automatic creation of semantic indices and annotations in an attempt to bridge 
      the multimedia semantic gap and thus provide corresponding solutions using 
      Semantic Web Technologies.
     </p>
     <p>
      For multimedia data retrieval, based on only low-level features as in the case 
      of "quering by example" and of content-based retrieval paradigms and systems, 
      on the one hand, one gets the advantage of an automatic computation of the 
      required low-level features but on the other hand, such methodology lacks the 
      ability to respond to high-level, semantic-based queries, and evidently loses 
      the relation among low-level multimedia features such as pitch, or 
      zero-crossing rate in audio or color and shape in image and video, or frequency 
      of words in text, to high-level domain concepts that essentially characterize 
      the underlying knowledge in data that a human is capable of quickly grasping, 
      whereas a machine cannot. For this reason, an abstraction of high level 
      multimedia content descriptions and semantics is required based on what can 
      actually be generated automatically, such as low-level features after low-level 
      processing, and on methods, tools and languages to represent the domain 
      ontology and attain the mapping between the two. Tha latter is needed so that 
      semantic indices are extracted as automatic as possible, rather than being 
      produced manually which is a time-consuming and not always efficient task 
      (attains a lot of subjective annotations). To avoid the latter limitations of 
      manual semantic annotations on multimedia data, metadata standards and 
      ontologies (upper, domain, etc.) have to be used and interoperate. Thus, a 
      requirement emerges for multimedia semantics interoperability to further enable 
      efficient solutions interoperation, when considering the distributed nature of 
      the Web and the enormous amounts of multimedia data published there.
     </p>
     <p>
      An example solution for the interoperability problem stated above is the MPEG-7 
      standard. MPEG-7, composed of various parts, defines both metadata descriptors 
      for structural and low-level aspects of multimedia documents, as well as high 
      level description schemes (Multimedia Description Schemes) for a higher-level 
      of descriptions including semantics of multimedia data. However, it does not 
      determine the mapping of the former to the latter based on the addressed 
      application domain. A number of publications have appeared to define the MPEG-7 
      core ontology to address such issues. What is 
      important is that the MPEG-7 provides the standardised means of descriptors 
      both low-level and high level. The value sets of those descriptions along with 
      a richer set of relationships definitions could form the necessary missing 
      piece along with the knowledge discovery algorithms which will use these to 
      extract semantic descriptions and indices in an almost automatic way out of 
      multimedia data. The bottom line thus is that MPEG-7 metadata descriptions need 
      to be properly linked to domain-specific ontologies that model high-level 
      semantics.
     </p>
     <p>
      Furthermore, one should consider usually the multimodality feature of 
      multimedia data and content on the Web. The same concept there may be described 
      by different means, that is by news in text as well as an image showing a 
      snapshot of what the news are reporting. Thus, since the provision of 
      cross-linking between different media types or corresponding modalities 
      supports a rich scope for inferencing a semantic interpretation, 
      interoperability between different single media schemes (audio ontology, text 
      ontology, image ontology, video ontology, etc.) is an important issue. This 
      emerges from the need to homogenise different single modalities for which it is 
      possible that:
     </p>
     <ol type="i">
      <li>
       Can infer particular high level semantics with different degrees of confidence 
       (e.g. rely mainly on audio for infering certain concepts than text),
      </li>
      <li>
       Can be supported by a world modelling (or ontologies) where different 
       relationships exist, e.g. in an image one can attribute spatial relationships 
       while in a video sequence spatio-temporal relationships can be attained, and
      </li>
      <li>
       Can have different role in a cross-modality fashion &#xe2;&#x80;&#x93; which modality triggers 
       the other, e.g. to identify that a particular photo in a Web page depicts 
       person X, we first extract information from text on the person's identity and 
       thereafter we cross-validate by the corresponding information extraction from 
       the image.
      </li>
     </ol>
     <p>Both of the above concerns, either the single modality tackled first or the 
      cross-modality (which essentially encapsulates the sinlge modality), require 
      semantic interoperability which will support a knowledge representation of the 
      domain concepts and relatioships, of the multimedia descriptors and of the 
      cross-linking of both, as well as a multimedia analysis part combined with 
      modeling, inferencing and mining algorithms that can be directed towards 
      automatic semantics extraction from multimedia to further enable efficient 
      semantic-based indexing and intelligent multimedia retrieval.
     </p>

     <h4 id="retrieval-scenario">Motivating Examples</h4>
     <p>
      In the following, current pitfalls with respect to the desired semantic 
      interoperability are given via examples. The discussed pitfalls are not the 
      only ones, therefore, further discussion is needed to cover the broad scope of 
      semantic multimedia analysis and retrieval.
     </p>

     <h5 id="retrieval-example1">Example 1: Single modality case: Lack of semantics in low-level descriptors</h5>
     <p>
      The linking of low-level features to high-level semantics can be obtained by 
      the following two main trends:
     </p>
     <ol type="i">
      <li>
       Using machine learning and mining techniques to infer the required mapping, 
       based on a basic knowledge representation of the concepts of the addressed 
       domain (usually low-to-medium level inferencing) and
      </li>
      <li>
       Using ontology-driven approaches to both guide the semantic analysis and infer 
       high-level concepts using reasoning and logics. This trend can include the 
       first one as well and then be further driven by medium-level semantics to more 
       abstract domain concepts and relationships.
      </li>
     </ol>
     <p>
      In both trends, it is appropriate for granularity purposes to produce 
      concept/event detectors, which usually incorporate a training phase applied on 
      training feature sets for which ground-truth is available (apriori knowledge of 
      addressed concepts or events). This phase enable optimization of the underlying 
      artificial intelligence algorithms. Semantic interoperability cannot be 
      achieved by only exchanging low-level features, wrapped in standardised 
      metadata descriptors, between different users or applications, since there is a 
      lack of formal semantics. In particular, a set of low level descriptors (eg. 
      MPEG-7 audio descriptors) cannot be semantically meaningful since there is a 
      lack of intuitive interpretation to higher levels of knowledge - these have 
      been however extensively used in content-based retrieval that relies on 
      similarity measures. The low level descriptors are represented as a vector of 
      numerical values, and thus, they are useful for a content-based multimedia 
      retrieval rather than a semantic multimedia retrieval process.
     </p>
     <p>
      Furthermore, since a set of optimal low level descriptors per target 
      application (be it music genre recognition or speaker indexing) can be 
      conceived by only multimedia analysis experts, this set has to be transparent 
      to any other user. For example, although a non-expert user can understand the 
      color and shape of a particular object, he is unable to attribute to this 
      object a suitable representation by the selection of appropriate low level 
      descriptors. It is obvious that the low level descriptors do not only lack 
      semantics but also limit their direct use to people that have gained a 
      particular expertise concerning multimedia analysis and multimedia 
      characteristics.
     </p>
     <p>The problem raised out of this example that needs to be solved is in <em>which 
       way low level descriptors can be efficiently and automatically linked and 
       turned into an exchangeable bag of semantics</em>.
     </p>

     <h5 id="retrieval-example2">Example 2: Multi-modality case: Fusion and interchange of semantics among media</h5>
     <p>
      In multimedia data and web content, cross-modality aspects are dominant, a 
      characteristic that can be efficiently exploited by semantic multimedia 
      analysis and retrieval, when all modalities can be exploited to infer the same 
      or related concepts or events. One aspect, is again motivated from the analysis 
      part, that refers to particular concepts and relationships capturing, which 
      require a priority in the processing of modalities during their automatic 
      extraction. For example, to enhance recognition of a face of a particular 
      person in an image appearing in a Web page, which is actually a very difficult 
      task, it seems more natural and efficient that initially inferencing is based 
      on the textual content, to locate the identity (name) of the person, and 
      thereafter, the results can be validated or enhanced by related results from 
      image analysis. Similar multimodal media analysis benefits can be obtained by 
      analysing synchronized audio-visual content to semantically annotate it. The 
      trends there are:
     </p>
     <ol type="i">
      <li>
       To conscruct combined feature vectors from audio and visual features and feed 
       those to machine learning algorithms to extract combined semantics
      </li>
      <li>
       To analyse each single modality separately towards recognizing medium-level 
       semantics or the same concepts and then fuse results of analysis (decision 
       fusion) in usually a weighted or ordered manner (depending on the underlying 
       single modality cross-relations towards the same topic) to either improve the 
       accuracy of semantics extraction results or enrich them, towards higher level 
       semantics.
      </li>
     </ol>
     <p>
      For the sake of clarity, an example scenario is described in the following 
      which is taken from the &#xe2;&#x80;&#x98;sports&#xe2;&#x80;&#x99; domain and more specifically from &#xe2;&#x80;&#x98;athletics&#xe2;&#x80;&#x99;.
     </p>
     <p>
      Let's assume that we need to semantically index and annotate, in the most 
      possible automatic way, the web page shown at Figure 1, which is taken from the 
      site of the <a href="http://www.iaaf.org/">International Association of Athletics Federation</a>. The subject 
      of this page is "the victory of the athlete Reiko Sosa at the Tokyo&#xe2;&#x80;&#x99;s 
      marathon". Let's try to answer the question: What analysis steps are required 
      if we would like to enable semantic retrieval results for the query "show me 
      images with the athlete Reiko Sosa" ?
     </p>
     <p>
      One might notice that for each image in this web page there is a caption which 
      includes very useful information about the content of the image, in particular 
      the persons appearing in it, i.e. structural (spatial) relations of the 
      media-rich web page contents. Therefore, it is important to identify the areas 
      of an image and the areas of a caption. Let's assume that we can detect those 
      areas (it is not useful to get into details how). Then, we proceed in the 
      semantics extraction of the textual content in the caption which identifies:
     </p>
     <ul>
      <li>
       Person Names = {Naoko Takahashi, Reiko Sosa},
      </li>
      <li>
       Places = {Tokyo}, Athletics type = {Women&#xe2;&#x80;&#x99;s Marathon} and
      </li>
      <li>
       Activity = {runs away from} (see Figure 1, in yellow and blue color).
      </li>
     </ul>
     <p>
      In the case of the semantics extraction from images, we can identify the 
      following concepts and relationships:
     </p>
     <ul>
      <li>
       In the image at the upper part of the web page, we can get the &#xe2;&#x80;&#x98;athlete&#xe2;&#x80;&#x99;s 
       faces&#xe2;&#x80;&#x99; and with respect to the spatial relationship of those faces we can 
       identify which face (athlete) takes lead against the other. Using only the 
       image we cannot draw a conclusion who is the athlete.
      </li>
      <li>
       In the image at the lower part of the web page, we can identify that there 
       exist a person after a face detection but still, we cannot ensure to whom this 
       face belongs to.
      </li>
     </ul>
     <p>
      If we combine both the semantics from textual information in captions and the 
      semantics from image we may give a large support to reasoning mechanisms to 
      reach the conclusion that "we have images with the athlete Reiko Sosa". 
      Nonetheless, in the case that we have several athletes like in the image on the 
      upper web image part, reasoning using the identified spatial relationship can 
      spot which particular athlete between the two, is Reiko Sosa.
     </p>

  <div style="border: 1px solid gray; padding: 1%; margin: 1%">
    <center>
     <img src="retrieval-athletics.jpg" alt="Example of a web page about athletics"/>
     <br/>
     Example of a web page about athletics.
    </center>     
  </div>

     <p>
      Another scenario involved multimodal analysis of audio-visual data, distributed 
      on the web or accessed through it from video archives, and concerns automatic 
      semantics extraction and annotation of video scenes related to violence, for 
      further purposes of content filtering and parental control [<a href="#Perperis">Perperis</a>]. Thus, the goal 
      in this scenario is automatic identification and semantic classification of 
      violent content, using features extracted from visual, auditory and textual 
      modalities of multimedia data.
     </p>
     <p>
      Let's consider that we are trying to automatically identify violent scenes 
      where fighting among two persons takes place with no weapons involved. The 
      low-level analysis parts will lead to different low-level descriptors 
      separately for each modality. For example, for the visual modality the analysis 
      will involve:
     </p>
     <ul>
      <li>
       Shot cut detection and video segmentation.
      </li>
      <li>
       Human body recognition and motion analysis.
      </li>
      <li>
       Human body parts recognition (arms, legs).
      </li>
      <li>
       Human body parts movement and tracking (i.e. "Fast horizontal hand movement")
      </li>
      <li>
       Interpretation of simple "visual" events/concepts based on spatial and temporal 
       relations of identified objects (medium-level semantics).
      </li>
     </ul>
     <p>
      On the other hand, the analysis of the auditory modality will involve:
     </p>
     <ul>
      <li>
       Audio signal segmentation.
      </li>
      <li>
       Segment classification in sound categories, including speech, silence, music, 
       scream, etc. which may relate to violence events or not (medium-level 
       semantics).
      </li>
     </ul>
     <p>
      Now, by of course fusing medium-level semantics and results from the single 
      modality analysis, taking under consideration spatio-temporal relations and 
      behaviour patterns, we evidently can automatically extract (infer) higher level 
      semantics. For example, the "punch" concept can be automatically extracted 
      based on the initial analysis results and on the sequence or synchronicity of 
      audio or visual detected events such as two person in visual data, the one 
      moving towards the other, while a punch sound and scream of pain is detected in 
      the audio data.
     </p>
     <p>To fulfil such scenarios as the ones presented above, we should solve the 
      problem <em>how to fuse and interchange semantics from different modalities</em>.
     </p>

     <h4 id="retrieval-solution">Possible Solutions</h4>

     <h5 id="retrieval-solution1">Example 1</h5>
     <p>
      As it was mentioned in Example 1, semantics extraction can be achieved via 
      concept detectors after a training phase based upon feature sets. Towards this 
      goal, recently there was a suggestion in [<a href="#Asbach">Asbach</a>] to go from a low level description 
      to a more semantic description by extending MPEG-7 to facilitate sharing 
      classifier parameters and class models. This should occur by presenting the 
      classification process in a standardised form. A classifier description must 
      specify on what kind of data it operates, contain a description of the feature 
      extraction process, the transformation to generate feature vectors and a model 
      that associates specific feature vector values to an object class. For this, an 
      upper ontology could be created, called a classifier ontology, which could be 
      linked to a multimedia core ontology (eg. CIDOC CRM ontology), a visual 
      descriptor ontology [<a href="#VDO">VDO</a>] as well as a domain ontology. A similar approach is 
      followed by the method presented in [<a href="#Tsekeridou">Tsekeridou</a>], where classifiers are used to 
      recognize and model music genres for efficient music retrieval, and description 
      extensions are introduced to account for such extended functionalities.
     </p>
     <p>
      As to these aspects, the current Use Case relates at some extend to the 
      Algorithm Representation UC. However, the latter refers mainly to general 
      purpose processing and analysis and not to analysis and semantics extraction, 
      based on classification and machine learning algorithms, to enable intelligent 
      retrieval.
     </p>
     <p>In the proposed solution, the visual descriptor ontology consists of a 
      superset of MPEG-7 descriptors since the existing MPEG-7 descriptors cannot 
      always support an optimal feature set for a particular class.</p>
     <p>A scenario that exemplifies the use of the above proposal is given in the 
      following. Maria is an architect who wishes to retrieve available multimedia 
      material of a particular architecture style like &#xe2;&#x80;&#x98;Art Nouveau&#xe2;&#x80;&#x99;, &#xe2;&#x80;&#x98;Art Deco&#xe2;&#x80;&#x99;, 
      &#xe2;&#x80;&#x98;Modern&#xe2;&#x80;&#x99; among the bulk of data that she has already stored using her 
      multimedia management software. Due to its particular interest, she plugs in 
      the &#xe2;&#x80;&#x98;Art Nouveau classifier kit&#xe2;&#x80;&#x99; that enables the retrieval of all images or 
      videos that correspond to this particular style in the form of visual 
      representation or non-visual or their combination (eg. a video on exploring the 
      House of V. Horta, a major representative of Art Nouveau style in Brussels, 
      which includes visual instances of the style as well as a narration about Art 
      Nouveau history).
     </p>
     <p>
      Necessary attributes for the classifier ontology are estimated to be:
     </p>
     <ul>
      <li>
       The name and category of the Classifier
      </li>
      <li>
       The list and types of input parameters
      </li>
      <li>
       The output type
      </li>
      <li>
       Limitations on data set, on value ranges for parameters, on processing time and 
       memory requirements
      </li>
      <li>
       Permormance metrics
      </li>
      <li>
       Guidelines of use
      </li>
      <li>
       Links to class models per domain/application and feature sets
      </li>
     </ul>
     <p>
      In the above examples, the exchangeable bag of semantics is directly linked to 
      an exchangeable bag of supervised classifiers.
     </p>

     <h5 id="retrieval-solution1">Example 2</h5>
     <p>
      In this example, to support reasoning mechanisms, it is required that apart 
      from the ontological descriptions for each modality, there is a need for a 
      cross-modality ontological description which interconnects all possible 
      relations from each modality and constructs rules that are cross-modality 
      specific. It is not clear, whether this can be achieved by an upper multimedia 
      ontology or a new cross-modality ontology that will strive toward the 
      knowledge representation of all possibilities combining media. It is evident 
      though, that the cross-modality ontology, along with the single modality ones, 
      greatly relate to the domain ontology, i.e. to the application at hand.
     </p>
     <p>
      Furthermore, in this new cross-modality ontology, special attention should be 
      taken for the representation of the priorities/ordering among modalities for 
      any multimodal concept (eg. get textual semantics first to attach semantics in 
      an image). This translates to sequential rules construction. However there are 
      cases, where simultaneous semantic instances in different modalities may lead 
      to higher level of semantics, that synchronicity is also a relationship to be 
      accounted for. Apart from the spatial, temporal or spatio-temporal 
      relationships that need to be accounted for, there is also the issue of 
      importance of each modality for identifying a concept or semantic event. This 
      may be represented by means of weights.
     </p>
     <p>The solution is composed also by relating visual, audio, textual descriptor 
      ontologies with a cross-modality ontology showcasing their inter-relations as 
      well as a domain ontology representing the concepts and relations of the 
      application at hand.</p>

     <!-- ======================================================================== -->
     <h3>
      <a name="algorithm">2.6 Use Case: Algorithm Representation</a>
     </h3>

     <h4 id="algorithm-introduction">Introduction</h4>
     <p>The problem is that algorithms for image analysis are difficult to manage, 
      understand and apply, particularly for non-expert users. For instance, a 
      researcher needs to reduce the noise and improve the contrast in a radiology 
      image prior to analysis and interpretation but is unfamiliar with the specific 
      algorithms that could apply in this instance. In addition, many applications 
      require the processes applied to media to be concisely recorded for re-use, 
      re-evaluation or integration with other analysis data. Quantifying and 
      integrating knowledge, particularly visual outcomes, about algorithms for media 
      is a challenging problem.
     </p>

     <h4 id="algorithm-solution">Solution</h4>
     <p>Our proposed solution is to use an algorithm ontology to record and describe 
      available algorithms for application to image analysis. This ontology can then 
      be used to interactively build sequences of algorithms to achieve particular 
      outcomes. In addition, the record of processes applied to the source image can 
      be used to define the history and provenance of data.
     </p>
     <p>The algorithm ontology should consist of information such as:
     </p>
     <ul>
      <li>
       name
      </li>
      <li>
       informal natural language description
      </li>
      <li>
       formal description
      </li>
      <li>
       input format
      </li>
      <li>
       output format
      </li>
      <li>
       example media prior to application
      </li>
      <li>
       example media after application
      </li>
      <li>
       goal of the algorithm
      </li>
     </ul>
     <p>To achieve this solution we need:
     </p>
     <ul>
      <li>
       a sufficiently detailed and well-constructed algorithm ontology;
      </li>
      <li>
       a core multimedia ontology;
      </li>
      <li>
       domain ontologies and;
      </li>
      <li>
       the underlying interchange framework supplied by semantic web technologies such 
       as XML and RDF.
      </li>
     </ul>
     <p>The benefits of this approach are a modularity through the use of independent ontologies to ensure usability and 
       flexibility.
     </p>
       
     <h4 id="algorithm-soa">State of the Art and Challenges</h4>
     <p>Currently there exists a taxonomy/thesaurus for image analysis algorithms we 
      are working on [<a href="#Asirelli">Asirelli</a>] but this is insufficient to support the required functionality. We are 
      collaborating on expanding and converting this taxonomy to an OWL ontology.
     </p>
     <p>The challenges are:
     </p>
     <ul>
      <li>
       to articulate and quantify the &#xe2;&#x80;&#x98;visual&#xe2;&#x80;&#x99; result of applying algorithms;
      </li>
      <li>
       to associate practical example media with the algorithms specified;
      </li>
      <li>
       to integrate and harmonise the ontologies;
      </li>
      <li>
       to reason with and apply the knowledge in the algorithm ontology (e.g. using 
       input and output formats to align processes).
      </li>
     </ul>

     <h4 id="algorithm-applications">Possible Applications</h4>
     <p>The formal representation of the semantics of algorithms enables recording of 
      provenance, provides reasoning capabilities, facilitates application and 
      supports interoperability of data. This is important in fields such as:
     </p>
     <ol type="1">
      <li>
       Smart assistance to support quality control and defect detection of complex, 
       composite, manufactured objects;
      </li>
      <li>
       Biometrics (face recognition, human behaviour, etc.)
      </li>
      <li>
       The composition of web services to automatically analyse media based on user 
       goals and preferences;
      </li>
      <li>
       To assist in the formal definition of protocols and procedures in fields that 
       are heavily dependent upon media analysis such as scientific or medical 
       research.
      </li>
     </ol>
     <p>These are applications that utilise media analysis and need to integrate 
      information from a range of sources. Often recording the provenance of 
      conclusions and the ability to duplicate and defend results is critical.
     </p>
     <p>For example, in the field of aeronautical engineering, aeroplanes are 
      constructed from components that are manufactured in many different locations. 
      Quality control and defect detection requires data from many disparate sources. 
      An inspector should understand the integrity of a component by acquiring local 
      data (images and others) and combining it with information from one or more 
      databases and possibly interaction with an expert.</p>

     <h4 id="algorithm-example">Example</h4>

     <div style="float: right; width: 45%; border: 1px solid gray; padding: 1%; margin: 1%">
       <img src="algorithm.jpg" alt="Excerpt of an Algorithm Ontology"/>
       <br/>
       Excerpt of an Algorithm Ontology.
     </div>
          
     <p>Problem:
     </p>
     <ul>
      <li style="list-style-type: none;">
       Suggest possible clinical descriptors (pneumothorax) given a chest x-ray.
      </li>
     </ul>
     <p>Hypothesis of solution :
     </p>
     <ul>
      <li style="list-style-type: none;">
       1) Get a digital chest x-ray of patient P (image A).</li>
      <li style="list-style-type: none;">
       2) Apply on image A a 
       digital filter to improve the signal-to-noise ratio (image B).</li>
      <li style="list-style-type: none;">
       3) Apply on 
       image B a region detection algorithm. This algorithm segments image B according 
       to a partition of homogeneous regions (image C).</li>
      <li style="list-style-type: none;">
       4) Apply on image C an 
       algorithm that 'sorts' according to a given criterion the regions by their 
       geometrical and densitometric properties (from largest to smallest, from 
       darkest to clearest, etc.) (array D).</li>
      <li style="list-style-type: none;">
       5) Apply on array D an algorithm that 
       searching on a database of clinical descriptors detects the one that best fits 
       the similarity criterion (result E).</li>
     </ul>
     <p>However, we should consider the following aspects:
     </p>
     <ul>
      <li style="list-style-type: none;">
       step 2) Which digital filter should be applied on image A? We can consider 
       different kinds of filters (Fourier, Wiener, Smoothing, etc. ) each one having 
       different input-output formats and giving slightly different results.</li>
      <li style="list-style-type: none;">
       step 3) 
       Which segmentation algorithm should be used? We can consider different 
       algorithms (clustering, histogram, homogeneity criterion, etc.).</li>
      <li style="list-style-type: none;">
       step 4) How 
       can we define geometrical and densitometric properties of the regions? There 
       are several possibilities depending on the considered mathematical models for 
       describing closed curves (regions) and the grey level distribution inside each 
       region (histogram, Gaussian-like, etc.).</li>
      <li style="list-style-type: none;">
       step 5) How can we define similarity 
       between patterns? There are multiple approaches that can be applied (vector 
       distance, probability, etc.).</li>
     </ul>
     <p>Each step could be influenced by the previous ones.
     </p>

     <p>Goal: to segment the chest x-ray image (task 3)
     </p>
     <p>A segmentation algorithm is selected. To be most effective this segmentation 
      algorithm requires a particular level of signal-to-noise ratio. This is defined 
      as the precondition (Algorithm.hasPrecondition) of the segmentation algorithm 
      (instanceOf.segmentationAlgoritm). To achieve this result a filter algorithm is 
      found (Gaussian.instanceOf.filterAlgorithm) which has the effect 
      (Algorithm.hasEffect) of improving the signal-to-noise ratio for images of the 
      same type as the chest x-ray image (Algorithm.hasInput). By comparing the 
      values of the precondition of the segmentation algorithm with the effect of the 
      filter algorithm we are able to decide on the best algorithms to achieve our 
      goal.
     </p>

     <h4 id="algorithm-interoperability">Interoperability aspects</h4>
     <p>Two types or levels of interoperability to be considered:
     </p>
     <ul>
      <li style="list-style-type: none;">
       1) low-level interoperability, concerning data formats and algorithms, their 
       transition or selection aspects among the different steps and consequently the 
       possible related ontologies (algorithm ontology, media ontology);</li>
      <li style="list-style-type: none;">
       2) high-level 
       interoperability, concerning the semantics at the base of the domain problem, 
       that is how similar problems (segment this image; improve image quality) can be 
       faced or even solved using codified 'experience' extracted from well-known case 
       studies.</li>
     </ul>
     <p>In our present use case proposal we focused our attention mainly on the 
      latter.
     </p>
     <p>Considering for instance the pneumothorax example, this can be studied 
      starting from a specific pre-analyzed case in order to define a general 
      reference procedure: what happens if we have to study a pneumothorax case 
      starting from an actual arbitrary image of a patient? Applying simply the 
      general procedure will not give in general the right solution because each 
      image (i.e. each patient) has its own specificity and the algorithms have to be 
      bound to the image type. Thus, the general procedure is not the one which fits 
      for any case because the results depend on the image to be processed. And also 
      in the better case, the result would be supervised and it would be necessary to 
      apply another algorithm to improve the result itself. High-level 
      interoperability would involve also a procedure able to take trace of a 
      specific result and how it has been obtained starting from a particular input.
     </p>
     <p>The open research questions that we are currently investigating relate to the 
      formal description of the values of effect and precondition and how these can 
      be compared and related. The interoperability of the media descriptions and 
      ability to describe visual features in a sufficiently abstract manner are key 
      requirements.</p>
      
     <!-- ======================================================================== -->
     <h2>
      <a name="openIssues">3. Open Issues</a>
     </h2>

     <h3>
      <a name="authoring">3.1 Semantics From Multimedia Authoring</a>
     </h3>
     
     <h4 id="authoring-introduction">Introduction</h4>
     <p>Authoring of personalized multimedia content can be considered as a process 
      consisting of selecting, composing, and assembling media elements into coherent 
      multimedia presentations that meet the user&#xe2;&#x80;&#x99;s or user group&#xe2;&#x80;&#x99;s preferences, 
      interests, current situation, and environment. In the approaches we find today, 
      media items and semantically rich metadata information are used for the 
      selection and composition task.
     </p>
     <p>For example, Mary authors a multimedia birthday book for her daughter's 18th 
      birthday with some nice multimedia authoring tool. For this she selects images, 
      videos and audio from her personal media store but also content which is free 
      or she own from the Web. The selection is based of the different metadata and 
      descriptions that come with the media such as tags, descriptions, the time 
      stamp, the size, the location of the media item and so on. In addition to the 
      media elements used Mary arranges them in a spatio-temporal presentation: A 
      welcome title first and then along "multimedia chapters" sequences and groups 
      of images interleaved by small videos. Music underlies the presentation. Mary 
      arranges and groups, adds comments and titles, resizes media elements, brings 
      some media to front, takes others into the back. And then, finally, there is 
      this great birthday presentation that shows the years of her daughter's life. 
      She presses a button, creates a Flash presentation and all the authoring 
      semantics are gone.
     </p>

     <h4 id="authoring-semantics">Lost multimedia semantics</h4>
     <p>Metadata and semantics today is mainly seen on the monomedia level. Single 
      media elements such as image, video and text are annotated and enriched with 
      metadata by different means ranging from automatic annotation to manual 
      tagging. In a multimedia document typically a set of media items come together 
      and are arranged into a coherent story with a spatial and temporal layout of 
      the time-continuous presentation, that often also allows user interaction. The 
      authored document is more than "just" the sum of the media elements it becomes 
      a new document with its own semantics. However, in the way we pursue multimedia 
      authoring today, we do not care and lose the emergent sementics from multimedia 
      authoring.
     </p>

     <h5 id="authoring-composition">Multimedia authoring semantics do not "survive" the composition</h5>
     <p>So, most valuable semantics for the media elements and the resulting 
      multimedia content that emerge with and in the authoring process are not 
      considered any further. This means that the effort for semantically enriching 
      media content comes to a sudden halt in the created multimedia document &#xe2;&#x80;&#x93; which 
      is very unfortunate. For example, for a multimedia presentation it could be 
      very helpful if an integrated annotation tells something about the structure of 
      the presentation, the media items and formats used, the lenght of the 
      presentation, its degree of interactivity, the table of contents of index of 
      the presentation, a textual summary of the content, the targeted user group and 
      so on. Current authoring tools just use metadata to select media elements and 
      compose them into a multimedia presentation. They do not extract and summarize 
      the semantics that emerge from the authoring and add them to the created 
      document for later search, retrieval and presentation support.
     </p>

     <h5 id="authoring-usage">Multimedia content can learn from composition and media usage</h5>
     <p>For example, the media store of Mary could "learn" that some of the media 
      items seem to be more relevant than others. Additional comments on parts of the 
      presentation could also be new metadata entries for the media items. And also 
      the metadata of the single media items as well as of the presentation are not 
      added to the presentation such that is can afterwards more easier be shared, 
      searched, managed.
     </p>

     <h4 id="authoring-interoperability">Interoperability problems</h4>
     <p>Currently, multimedia documents do not come with a single annotation scheme. 
      SMIL comes with the most advanced modeling of annotation. Based on RDF, the 
      head of a SMIL document allows to add an RDF description of the presentation to 
      the structured multiemdia document and gives the author or authoring tool a 
      space where to put the presentation's semantics. In specific domains we find 
      annotation schemes such as 
      <a href="http://ltsc.ieee.org/wg12/files/LOM_1484_12_1_v1_Final_Draft.pdf">LOM</a> 
      that provide the vocabulary for annotating 
      Learning Objects which are often Powerpoint Presentations of PDF documents but 
      might well be multimedia presentations. 
      <a href="http://www.dcs.shef.ac.uk/~ajay/html/cresearch.html">AKtive Media</a> is an ontology based 
      multimedia annotation (Images and Text) system which provides an interface for 
      adding ontology-based, free-text and relational annotations within multimedia 
      documents. Even though the community effort will contribute to a more or less 
      unified set of tags, this does not ensure interoperability, search, and 
      exchange.
     </p>
     
     <h4 id="authoring-needs">What is needed</h4>
     <p>A semantic description of multimedia presentation should reveal the semantics 
      of its content as well as of the composition such that a user can search, 
      reuse, integrate multimedia presentation on the Web into his or her system. A 
      unified semantic Web annotation scheme could then describe the thousands of 
      Flash presentations as well as powerpoints presentation, but also SMIL and SVG 
      presentations. For existing presentations this would give the authors a chance 
      to annotate the presentations. For authoring tool creators this will give the 
      chance to publish a standardized semantic presentation description with the 
      presentation.
     </p>
     
     <!-- ======================================================================== -->     
     
     <h3>
      <a name="multimedial">3.2 Building Multimedial Semantic Web Applications</a>
     </h3>

     <h4 id="multimedial-introduction">Introduction</h4>
     <p>This use case is all about supporting to build real distributed, Semantic Web 
      applications in the domain of multimedial content. It discusses scalability, 
      and interop issues and tries to propose solutions to lower the barrier of 
      implementing such multimedial Semantic Web applications.
     </p>

     <h4 id="multimedial-motivation">Motivation</h4>
     <p>Shirin is a IT manager at a NGO, called FWW (Foundation for Wildlife in the 
      World) and wants to offer some new multimedial service to inform, alarm, etc. 
      members, e.g.:
     </p>
     <dl>
      <dt>Track your animal godchild (TyAG)</dt>
      <dd>
       <p>A service that would allow a member to audio-visually track his godchild 
        (using geo-spatial services, camera, satellite, RFID :). DONald ATOR, a 
        contributer of FWW is the godfather of a whale. Using the TyAG service he is 
        able to observe the route that his favorite whale takes (via <a href="http://www.geonames.org/">
         Geonames</a>) and in case that the godchild is near a FWW-observing point, 
        Donald might also see some video footage. Currently the whales are somewhere 
        around <a href="http://www.geonames.org/maps/geonameId=3426256">Thule 
         Island</a>. TyAG allows Donald to ask questions like: <em>When</em> will the 
        whales be <em>in my region</em>? etc.</p>
      </dd>
      <dt>Video-news (vNews)</dt>
      <dd>
       <p>As Donald has gathered some good experiences with TyAG, he wants to be 
        informed about news, upcoming events, etc. w.r.t. <em>whales</em>. The backbone 
        of the vNews system is smart enough to understand that <em>whales</em> are a 
        kind of <em>animals that live in the water</em>. Any time a FWW member puts 
        some footage on the FWW-net that has some <em>water animals</em> in it, vNews - 
        using some automated feature extraction utils - offers it to Donald as well to 
        view it. <strong>Note:</strong> There might be a potential use of the outcome 
        of the <a href="http://www.w3.org/2005/Incubator/mmsem/wiki/News_Use_Case">News 
         Use Case</a> here.
       </p>
      </dd>
      <dt>Interactive Annotation</dt>
      <dd>
       <p>A kind of video blogging [<a href="#Parker">Parker</a>] using vNews. Enables members to share thoughts about 
        endangered species etc. or to find out more information about a specific entity 
        in a (broadcasted) videostream. Therefore, vNews is able to automatically 
        segment its video-content and set up a list of <em>objects</em>, etc. For each 
        of the <em>objects</em> in a video, a user can get further information (by 
        linking it to Wikipedia, etc.) and share her thoughts about it with other 
        members of the vNews network.
       </p>
      </dd>
     </dl>

     <h4 id="multimedial-solution">Possible Solutions</h4>
     <p>Common to all services listed above is an ample infrastructure that has to 
      deal with the following challenges:
     </p>
     <ul>
      <li>
       Using many different (multimedial) metadata (EXIF, GPS-data, etc.) as input, a 
       common internal representation has to be found (e.g. MPEG-7) - 
       INTEROPERABILITY.
      </li>
      <li>
       For the domain (animals) a formal description needs to be defined - ONTOLOGY 
       ENGINEERING (also visual to entity mapping).
      </li>
      <li>
       <p>Due to the vast amount of metadata, a scaleable approach has to be taken that 
        can handle both <em>low-level</em> features (in MPEG-7) and <em>high-level</em> 
        features (in RDF/OWL) - SCALABILITY.
       </p>
      </li>
     </ul>
     <p>We now try to give possible answers to the above listed question to enable 
      Shirin to implement the services in terms of:
     </p>
     <ul>
      <li>
       Based on well-known ontology engineering methods give hints on how to modell 
       the domain.
       </li>
      <li>
       Giving support in selecting a representation that both addresses low-level as 
       high-level features.
       </li>
      <li>
       Supplying an SW-architect with an evaluation of RDF-stores w.r.t. multimedial 
       metadata.</li>
     </ul>
     
     <!-- ======================================================================== -->
     <h2>
      <a name="framework">4. Common Framework</a>
     </h2>
     <p>In this section, we will propose a common framework that seek to provide both 
      syntactic (via RDF) and semantic interoperability. During the FTF2, we have 
      identified several layers of interoperability. Our methodology is simple: each 
      use case identifies a common ontology/schema to facilitate interoperability in 
      its own domain, and then we provide a simple framework to integrate and 
      harmonise these common ontologies/schema from different domains. Furthermore, 
      the simple extensible mechanism is provided to accommodate other 
      ontologies/schema related to the use cases we considered. Last but not least, 
      the framework includes some guidelines on which standard to use for specific 
      tasks related to the use cases.
     </p>

     <h3>
      <a name="syntactic">4.1. Syntactic Interoperability: RDF</a>
     </h3>
     <p>
      Resource Description Framework (RDF) is a W3C recommendation that provides a 
      standard to create, exchange and use annotations in the Semantic Web. An RDF 
      statement is of the form [subject property object .] This simple and general 
      form of syntax makes RDF a good candidate to provide (at least) syntactic 
      interoperability.
     </p>
     
     <h3>
      <a name="layers">4.2. Layers of Interoperability</a>
     </h3>
     <p>
     [Based on discussions in FTF2]
     </p>

     <h3>
      <a name="common">4.3. Common Ontology/Schema</a>
     </h3>
     <p>
      Individual use case provides its common ontology/schema for its domain.
     </p>


     <h3>
      <a name="ontology">4.4. Ontology/Schema Integration, Harmonisation and Extension</a>
     </h3>
     <p>
      [Integrate and harmonise the common ontologies/schema presented in the 
      previous sub-section. Based on this, to provide a simple extensible mechanism.]
     </p>

     <h3>
      <a name="guidelines">4.5. Guidelines</a>
     </h3>
     <p>
      Individual use case provides guidelines on which standard to use for specific 
      tasks related to the use case.
     </p>

     <!-- ======================================================================== -->
     <h2>
      <a name="conclusion">5. Conclusion</a>
     </h2>
     

     <!-- ======================================================================== -->
     <h2>
      <a name="references">6. References</a>
     </h2>
     <dl>

   <dt>
    <a id="Asbach" name="Asbach">[Asbach]</a>
   </dt>
   <dd><span class="title">Object detection and classification based on MPEG-7 descriptions &#xe2;&#x80;&#x93; Technical study, use cases and business models</span>.   
   <span class="author">M. Asbach and J-R Ohm</span>. 
   ISO/IEC JTC1/SC29/WG11/MPEG2006/M13207, April 2006, Montreaux, CH.
   </dd>

   <dt>
    <a id="Asirelli" name="Asirelli">[Asirelli]</a>
   </dt>
   <dd><span class="title">An Infrastructure for MultiMedia Metadata Management</span>.   
   <span class="author">Patrizia Asirelli, Massimo Martinelli, Ovidio Salvetti</span>. 
   <i>In:</i> Proceedings of International SWAMM Workshop, 2006.
   </dd>

   <dt>
    <a id="Broekstra" name="Broekstra">[Broekstra]</a>
   </dt>
   <dd><span class="title">Sesame: A generic archistecture for storing and querying RDF and RDF schema</span>.   
   <span class="author">J. Broekstra, A. Kampman and F. van Harmelen</span>. 
   <i>In:</i> Proceedings of <a href="http://iswc2002.semanticweb.org/">The International Semantic Web Conference 2002</a> 
   (pages 54-68), 2002, Sardinia
   </dd>

   <dt>
    <a id="Declerck" name="Declerck">[Declerck]</a>
   </dt>
   <dd><span class="title">Translating XBRL Into Description Logic. An Approach Using Protege, Sesame &amp; OWL</span>.   
   <span class="author">T. Declerck and H.-U Krieger</span>. 
   <i>In:</i> Proceedings of the 9th International Conference on Business Information Systems, 2006
   </dd>

   <dt>
    <a id="DIG35" name="DIG35">[DIG35]</a>
   </dt>
   <dd>
    Digital Imaging Group (DIG),
    <a href="http://xml.coverpages.org/FU-Berlin-DIG35-v10-Sept00.pdf">DIG35 Specification - Metadata for Digital Images - Version 1.0 August 30, 2000</a>
   </dd>

   <dt>
    <a id="DublinCore" name="DublinCore">[Dublin Core]</a>
   </dt>
   <dd>
    The Dublin Core Metadata Initiative,
    <a href="http://dublincore.org/documents/dces/">Dublin Core Metadata Element Set</a>, Version 1.1: Reference Description
   </dd>

   <dt>
    <a id="EBU" name="EBU">[EBU]</a>
   </dt>
   <dd>
    European Broadcasting Union,
    <a href="http://www.ebu.ch/">http://www.ebu.ch/</a>
   </dd>

   <dt>
    <a id="Exif" name="Exif">[Exif]</a>
   </dt>
   <dd>
    Standard of Japan Electronics and Information Technology Industries Association,
    <a href="http://www.digicamsoft.com/exif22/exif22/html/exif22_1.htm">Exchangeable image file format for digital still cameras: Exif Version 2.2</a>
   </dd>

   <dt>
    <a id="Flickr" name="Flickr">[Flickr]</a>
    </dt>
   <dd>
    <a href="http://www.flickr.com/">Flickr</a> online photo management and sharing application,
    <a href="http://www.flickr.com/">http://www.flickr.com/</a>, Yahoo! Inc, USA
   </dd>

   <dt>
    <a id="fotocommunity" name="fotocommunity">[Foto Community]</a>
    </dt>
   <dd>
    Foto Community,
    <a href="http://www.fotocommunity.com/">http://www.fotocommunity.com/</a>
   </dd>

   <dt>
    <a id="GFK2006" name="GFK2006">[GFK]</a>
   </dt>
   <dd><span class="title">Usage behavior digital photography</span>.   
    GfK Group for CeWe Color, 2006
   </dd>

   <dt>
    <a id="Hildebrand" name="Hildebrand">[Hildebrand]</a>
   </dt>
   <dd><span class="title"><a href="http://dx.doi.org/10.1007/11926078_20">/facet: A browser for heterogeneous semantic web repositories</a></span>.   
   <span class="author">Michiel Hildebrand, Jacco van Ossenbruggen, and Lynda Hardman</span>. 
   <i>In:</i> <a href="http://iswc2006.semanticweb.org/">The Semantic Web - ISWC 2006</a> (pages 272-285), November 2006, Athens, USA.
   </dd>

   <dt>
    <a id="HitWise" name="HitWise">[HitWise]</a>
    </dt>
   <dd>
    HitWise Intelligence,
    <a href="http://weblogs.hitwise.com/leeann-prescott/2006/08/delicious_traffic_more_than_do.html">
    Del.icio.us Traffic More Than Doubled Since January</a>
   </dd>

   <dt>
    <a id="Hotho" name="Hotho">[Hotho]</a>
   </dt>
   <dd><span class="title">Information Retrieval in Folksonomies: Search and Ranking</span>.   
   <span class="author">A. Hotho, R. Jaschke, C. Schmitz and G. Stumme</span>. 
   <i>In:</i> The 3rd European Semantic Web Conference (ESWC), 2006 Budva, Montenegro.
   </dd>

   <dt>
    <a id="IIM" name="IIM">[IIM]</a>
   </dt>
   <dd><span class="title">Information Interchange Model</span>,
   <a href="http://www.iptc.org/IIM/">http://www.iptc.org/IIM/</a>,  
    International Press Telecommunication Council (IPTC)
   </dd>

   <dt>
    <a id="Koivunen" name="Koivunen">[Koivunen]</a>
   </dt>
   <dd><span class="title"><a href="http://www.annotea.org/eswc2005/01_koivunen_final.pdf">
   Annotea and Semantic Web Supported Collaboration</a></span>.   
   <span class="author">M. Koivunen</span>. 
   <i>In:</i> Proceedings of the European Semantic Web Conference (ESWC), Crete, 2005
   </dd>

   <dt>
    <a id="Knublauch" name="Knublauch">[Knublauch]</a>
   </dt>
   <dd><span class="title">Editing description logic ontologies with the Protege OWL plugin</span>.   
   <span class="author">H. Knublauch, M.A. Musen and A.L. Rector</span>. 
   <i>In:</i> Proceedings of the International Workshop on Description Logics (DL), 2004
   </dd>

   <dt>
    <a id="Lara" name="Lara">[Lara]</a>
   </dt>
   <dd><span class="title">XBRL Taxonomies and OWL Ontologies for Investment Funds</span>.
   <span class="author">R. Lara, I. Cantador and P. Castells</span>,
   <i>In:</i> ER (Workshops), (pages 271-280), 2006
   </dd>

   <dt>
    <a id="Mathes" name="Mathes">[Mathes]</a>
   </dt>
   <dd><span class="title"><a href="http://www.adammathes.com/academic/computer-mediated-communication/folksonomies.html">
   Folksonomies - Cooperative Classification and Communication Through Shared Metadata</a></span>.
   <span class="author">A. Mathes</span>, 
   Computer Mediated Communication - LIS590CMC, Graduate School of Library and Information Science, 
   University of Illinois Urbana-Champaign, 2004
   </dd>

   <dt>
    <a id="Mejias" name="Mejias">[Mejias]</a>
   </dt>
   <dd><span class="title">Tag literacy</span>.
   <span class="author">Ulises Ali Mejias</span>, 
   <a href="http://ideant.typepad.com/ideant/2005/04/tag_literacy.html">http://ideant.typepad.com/ideant/2005/04/tag_literacy.html</a>, 
   2005
   </dd>

   <dt>
    <a id="MPEG-7" name="MPEG-7">[MPEG-7]</a>
   </dt>
   <dd>
    Information Technology - Multimedia Content Description Interface (MPEG-7).
    Standard No. ISO/IEC 15938:2001, International Organization for Standardization(ISO), 2001
   </dd>

   <dt><a id="MMSEM-Image" name="MMSEM-Image"></a>[MMSEM Image]</dt>
   <dd>
    <cite>
     <a href="http://www.w3.org/2005/Incubator/mmsem/XGR-image-annotation-20070814/">
      Image Annotation on the Semantic Web</a>
    </cite>, Rapha&#235;l Troncy, Jacco van Ossenbruggen, Jeff Z. Pan and Giorgos Stamou, 
    Multimedia Semantics Incubator Group Report (XGR), 14 August 2007,
      <a href="http://www.w3.org/2005/Incubator/mmsem/XGR-image-annotation/">http://www.w3.org/2005/Incubator/mmsem/XGR-image-annotation/</a>
   </dd>

   <dt>
    <a id="Naphade" name="Naphade">[Naphade]</a>
    </dt>
   <dd>
    Extracting semantics from audiovisual content: The final frontier in multimedia retrieval,
    <span class="author">N. Naphade and T. Huang</span>. 
    <i>In:</i> IEEE Transactions on Neural Networks, vol. 13, No. 4, 2002.
   </dd>

   <dt>
    <a id="NetRatings" name="NetRatings">[NetRatings]</a>
    </dt>
   <dd>
    Nielsen/NetRatings,
    <a href="http://www.nielsen-netratings.com/pr/PR_060810.PDF">
    User-generated content drives halfs of US Top 10 fastest growing web brands</a>
   </dd>

   <dt>
    <a id="Newman" name="Newman">[Newman]</a>
   </dt>
   <dd>
    Richard Newman, Danny Ayers and Seth Russell.
    Tag Ontology, <a href="http://www.holygoat.co.uk/owl/redwood/0.1/tags/">http://www.holygoat.co.uk/owl/redwood/0.1/tags/</a>
   </dd>

   <dt>
    <a id="NewsML" name="NewsML">[NewsML-G2]</a>
   </dt>
   <dd>
    IPTC,
    <a href="http://www.iptc.org/NAR/">News Architecture (NAR) for G2-Standards Specifications (released 30th May, 2007)</a>
   </dd>

   <dt>
    <a id="NewsCodes" name="NewsCodes">[NewsCodes]</a>
   </dt>
   <dd>
    NewsCodes - Metadata taxonomies for the news industry,
    <a href="http://www.iptc.org/NewsCodes/">http://www.iptc.org/NewsCodes/</a>
   </dd>

   <dt>
    <a name="OWL" id="OWL">[OWL]</a>
   </dt>
   <dd>
     <cite>
       <a href="http://www.w3.org/TR/2004/REC-owl-ref-20040210/">
        OWL Web Ontology Language Reference</a></cite>, S. Bechhofer, F. van Harmelen, J. Hendler, I. Horrocks, 
        D.L. McGuinness, P.F. Patel-Schneider and L.A. Stein, Editors, W3C
        Recommendation, 10 February 2004,
        <a href="http://www.w3.org/TR/owl-ref/">http://www.w3.org/TR/owl-guide/</a>
   </dd>

   <dt>
    <a id="Pachet" name="Pachet">[Pachet]</a>
   </dt>
   <dd>
    Knowledge Management and Musical Metadata.
    F. Pachet, Encyclopedia of Knowledge Management, Schwartz, D. Ed. Idea Group, 2005
   </dd>

   <dt>
    <a id="Parker" name="Parker">[Parker]</a>
    </dt>
   <dd>
    <a href="http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1423925">Video blogging: Content to the max</a>,
    <span class="author">C. Parker and S. Pfeiffer</span>.
    IEEE MultiMedia, vol. 12, no. 2, pp. 4-8, 2005
   </dd>

   <dt>
    <a id="Perperis" name="Perperis">[Perperis]</a>
    </dt>
   <dd>
    Automatic Identification in Video Data of Dangerous to Vulnerable Groups of Users Content,
    <span class="author">T. Perperis and S. Tsekeridou</span>.
    Presentation at SSMS2006, Halkidiki, Greece, 2006
   </dd>

   <dt>
    <a id="PhotoRDF" name="PhotoRDF">[PhotoRDF]</a>
   </dt>
   <dd>
    W3C Note 19 April 2002,
    <a href="http://www.w3.org/TR/2002/NOTE-photo-rdf-20020419">Describing and retrieving photos using RDF and HTTP</a>
   </dd>

   <dt>
    <a id="Riya" name="Riya">[Riya]</a>
    </dt>
   <dd>
    Riya Foto Search,
    <a href="http://www.riya.com/">http://www.riya.com/</a>
   </dd>

   <dt>
    <a id="Segawa" name="Segawa">[Segawa]</a>
    </dt>
   <dd>
    <a href="http://doi.acm.org/10.1145/1135777.1135910">Web annotation sharing using P2P</a>,
    <span class="author">O. Segawa</span>. 
    <i>In:</i> Proceedings of the 15th International Conference on World Wide Web, pages 851-852, Edinburgh, Scotland, 2006.
   </dd>

   <dt>
    <a id="Smith" name="Smith">[Smith]</a>
    </dt>
   <dd>
    <a href="http://atomiq.org/archives/2004/08/folksonomy_social_classification.html">
    Atomiq: Folksonomy: social classification</a>,
    <span class="author">G. Smith</span>, August 2004.
   </dd>

   <dt>
    <a id="Schmitz" name="Schmitz">[Schmitz]</a>
    </dt>
   <dd>
    <a href="http://www.rawsugar.com/www2006/22.pdf">Inducing Ontology from Flickr Tags</a>,
    <span class="author">P. Schmitz</span>. 
    <i>In:</i> Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland, 2006.
   </dd>

   <dt>
    <a name="SKOS" id="SKOS">[SKOS]</a>
   </dt>
   <dd>
    SKOS Core, <a href="http://www.w3.org/2004/02/skos/core/">http://www.w3.org/2004/02/skos/core/</a>
   </dd>

   <dt>
    <a id="Trant" name="Trant">[Trant]</a>
    </dt>
   <dd>
    Exploring the potential for social tagging and folksonomy in art museums: proof of concept,
    <span class="author">J. Trant</span>. 
    <i>In:</i> New Review of Hypermedia and Multimedia, 2006    
   </dd>

   <dt>
    <a id="Tsekeridou" name="Tsekeridou">[Tsekeridou]</a>
    </dt>
   <dd>
    MPEG-7 based Music Metadata Extensions for Traditional Greek Music Retrieval,
    <span class="author">S. Tsekeridou, A. Kokonozi, K. Stavroglou and C. Chamzas</span>. 
    <i>In:</i> IAPR Workshop on Multimedia Content Representation, Classification and Security, Istanbul, Turkey, September 2006
   </dd>

   <dt>
    <a id="VDO" name="VDO">[VDO]</a>
   </dt>
   <dd>
    aceMedia Visual Descriptor Ontology, <a
    href="http://www.acemedia.org/aceMedia/reference/resource/index.html">
    http://www.acemedia.org/aceMedia/reference/resource/index.html</a>
   </dd>

   <dt>
    <a id="XBRL" name="XBRL">[XBRL]</a>
   </dt>
   <dd>
    XBRL - eXtensible Business Reporting Language,
    <a href="http://www.xbrl.org/Home/">http://www.xbrl.org/Home/</a>, see also 
    <a href="http://www.tbray.org/ongoing/When/200x/2006/10/04/XBRL-RSS">Tim Bray's blog</a>
   </dd>

   <dt>
    <a id="XMP" name="XMP">[XMP]</a>
   </dt>
   <dd>
    Adobe,
    <a href="http://partners.adobe.com/public/developer/en/xmp/sdk/XMPspecification.pdf">XMP Specification</a>
   </dd>

     </dl>

     <!-- ======================================================================== -->
     <h2>
      <a id="acknowledgments" name="acknowledgments">Acknowledgments</a>
     </h2>
     <p>
      The editors would like to thank all the contributors for the authoring of the 
      use cases (Melliyal Annamalai, George Anadiotis, Patrizia Asirelli, Susanne Boll, Oscar Celma, 
      Thierry Declerk, Thomas Franz, Christian Halaschek Wiener, Michael Hausenblas, Michiel Hildebrand, 
      Suzanne Little, Erik Mannens, Massimo Martinelli, Ioannis Pratikakis, Ovidio 
      Salvetti, Sofia Tsekeridou, Giovanni Tummarello) and the XG members for their 
      feedback on earlier versions of this document.
     </p>
     <hr />
     <p>$Id: Overview.html,v 1.8 2007/08/14 23:54:38 rtroncy Exp $</p>
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