=Paper=
{{Paper
|id=Vol-417/paper-1
|storemode=property
|title=Towards Increased Reuse: Exploiting Social and Content Related Features of Multimedia Content on the Semantic Web
|pdfUrl=https://ceur-ws.org/Vol-417/paper1.pdf
|volume=Vol-417
|dblpUrl=https://dblp.org/rec/conf/samt/Burger08
}}
==Towards Increased Reuse: Exploiting Social and Content Related Features of Multimedia Content on the Semantic Web==
https://ceur-ws.org/Vol-417/paper1.pdf
Towards Increased Reuse: Exploiting Social and
Content Related Features of Multimedia Content on the
Semantic Web
Tobias Bürger
Semantic Technology Institute (STI),
University of Innsbruck, Austria,
tobias.buerger@sti2.at
Abstract. While the amount of multimedia content on the Web is continuously
growing, reuse of multimedia content remains low and automated processing of
content remains hard. An increased reuse of content however would result in a
greater consistency, quality and lowered cost of the production of new content.
The retrieval of multimedia content on the Web is a continuous challenge which
is due to the lack of (formal) descriptions and generic multimedia analysis al-
gorithms. We present a model and a set of ontologies to mark up multimedia
content embedded in web pages which can be used to deploy its descriptions on
the Semantic Web and which in turn can be used to reason about the contents
of multimedia resources. This model is part of a method to raise the reusability
potential of content which in turn is expected to lower production costs of new
content.
1 Introduction
In 2001, Tim Berners-Lee et al. introduced their vision of an augmented Web in which
information is meaningful for machines as well as for humans. Since the introduction of
this vision, the Web more and more turned into a multimedia environment and became
a place to share great amounts of professionally and user generated content.
One of the challenges brought forward by the Semantic Web is the need to enrich
existing digital content in such a way that machines can determine what the content is
about, how it can be used, and whether one needs to pay for it or not. This part of the
vision, richly annotated and formally described content which supports its automated
negotiation, is sometimes referred to as Intelligent Content. Not only the ever growing
amount of digital content raised interest in this topic in the recent years, but also the
lack of appropriate multimedia description standards for the explication of features of
content [22, 16]. Relevant features include not only the semantic content of images but
also structural, legal or behavioral issues as especially multimedia content has many
characteristics that for different usage scenarios need to be described.
One question that we especially intend to answer is how multimedia content which
is published on the Web can be described to efficiently be reused, republished or refor-
matted for different purposes or different target media. And more concrete: How can
social and content-related features and descriptions of content on the Web be exploited
to increase the reusability potential of content.
9
Exploiting Social and Content Related Features of Multimedia Content on the SW
The contribution of this paper is a conceptual model to describe and represent mul-
timedia content including its context on the Web. The model is especially designed to
raise the reusability potential of content published in typical web pages (e.g. embedded
in news stories), social media or professional image licensing sites. The model is sup-
ported by a set of ontologies to mark up multimedia resources inline of HTML pages
using RDFa1 .
2 Motivating Scenario: Towards Web-scale Reusability of Content
Supporting the reuse of content can provide significant improvements in the way how
content is created and used, including increased quality and consistency, long-term re-
duced time and costs for development, maintenance, or adaptation to changing needs
[18]. The amount of content available on the Web grows every day and the amount of
professionally produced content in local or commercial databases also stays on a high
level all of which could potentially be reused. The wish for reuse of content comes in-
line with the need for automation of associated tasks like search & retrieval, selection,
or adaptation of content. However, automated handling is mostly hindered by the fact
that users search for content based on the aboutness of the contained information which
is – if at all – represented by tags attached to the content on the Web. Still, high-level
features which are of high importance for retrieval of content [13] are not automatically
derivable by most analysis algorithms which is due to the Semantic Gap [19], which
commonly refers to the large gulf between automatically extractable low-level features
and high-level semantics which are typically derived based on the background of a hu-
man being. Furthermore licenses and conditions of use are mostly encoded in web pages
using natural language which is understandable by humans but not by machines. With
the aim to automate handling which includes selection of the right pieces of content,
this fact demands for richer semantic descriptions of content. Having richer semantic
descriptions in turn implies improvements in reuse and automation.
Currently content on the Web is published based on different metadata standards and
with different intention in mind. End users either publish images for non-commercial
aspects, i.e. for others to watch or to gain reputation, or they publish it out of a com-
mercial reason, either because they want to sell it or to grant access in order to gain
revenues.
Current Web-based content reuse is difficult because amongst others
– Presentations are mostly available as PDF files with some tags attached to them.
However fine granular descriptions of images, which were used in some slides and
which would be candidates for reuse, are mostly missing.
– The same is true for videos: Tags are provided which are often not covering the
semantics of particular scenes but only of the whole video.
– Cross-site searches in commercial image libraries are often hindered by the fact
that images are described differently across sites.
– A huge amount of sites are using images for illustrative purposes which are not
explicitly described. Sometimes textual descriptions are provided with the images
but these are not explicitly assigned to the image which again makes retrieval hard.
1
http://www.w3.org/TR/xhtml-rdfa-primer/
10
Exploiting Social and Content Related Features of Multimedia Content on the SW
We intend to overcome these difficulties using a two-fold strategy in order to raise
the reusability rate of content: (1) Raise the findability rate of content by unlocking
the reusability potential of content published on social media, non-commercial or com-
mercial sites by formally describing their content related- and social features and (2)
Increase the ad-hoc adaptability of content by allowing to select and describe parts of it
instead of only the singular datastream / resource.
3 Requirements for Intelligent Content with respect to Reusability
Content is an individual securable and targeted reproduction of implicit information
done by humans. Important aspects of content, which are important with respect to
reuse, include:
– the contextual aspect (ie. what the information or the content is about),
– the technical aspect (ie. how is it technically represented,),
– the economic aspect (ie. what is the value of the content), and
– the legal aspect (ie. what are the rights to use the content)
Intelligent Content Objects (ICOs) – as we understand them – are inspired by the vision
of smart content objects [1], which define a package structure including the content,
knowledge about its properties and several interfaces to interact with the smart content
object. This is similar to packaging standards like MPEG-21 [4] or OAI Information
Packages [7] with the difference that semantic technologies are explicitly used to pro-
vide machine understandable descriptions.
Requirements for multimedia content descriptions have been researched before and in-
vestigations of the combination of multimedia descriptions with features from the Se-
mantic Web are yet numerous which we summarize in [5]. We want to highlight this
issue again with respect to reusability of content which we believe deserves special
attention.
We identified 3 aspects that need to be fulfilled in order to increase the reusability
potential of content:
1. Findability: Reuse is often hindered by the fact that people are not aware of con-
tent to be re-used because it can not be found. This is especially true for multimedia
content. Thus there is a need for a metadata model especially supporting findabil-
ity and reusability of content. The model has to support descriptive information
but also needs to support linking and referencing of secondary information and to
acknowledge the fact that on the Web2.0 different users may provide metadata by
tagging, rating, or referencing.
2. Adaptability: Resources published on the Web are mostly atomic. They are mostly
available in a single file even if the file has been assembled out of different datas-
treams. This makes content difficult to repurpose. The existence of a reusability-
friendly format that makes structure explicit could however enable the reuse of
components as well. Thus there is the need for a model that allows to access sub-
components and which enables to structure content and to identify and select its
sub-parts.
11
Exploiting Social and Content Related Features of Multimedia Content on the SW
3. Cross-Community Interoperability: Query mechanisms for content must reflect
habits of people from different communities. People from the E-Learning domain
are used to think in terms of learning objects and content fragments while people
from the archival domain communicate in terms of Information Packages or Infor-
mation Objects, etc. This demands for a basic compatibility with existing standards.
4 A Conceptual Model for Intelligent Content for the Semantic
Web
In this section we present a reference model for intelligent content which takes the
characteristics of the Semantic Web [5] and the characteristics of the Web 2.0 as being
a paradigm for rich social interaction on the Web into account.
4.1 A Data Model for Intelligent Content
Firstly there is a need for an abstract model that offers a set of well-defined concepts and
vocabularies to sketch the problem of how content can be described and how it supports
adaptability and findability while remaining compatible with existing data models from
the multimedia, E-Learning and archival domain to support interoperability.
The general aim is to lay a graph over published contents on the Web, associating
descriptions in a Web page to it and to bind metadata to content and descriptive in-
formation. This approach is similar to the way how digital assets are organized in the
information domain. Here digital assets aggregate multiple-streams of relevant data, de-
scriptive metadata and secondary data into one compound object which is then managed
by a single entity (cf. section 6).
In [3] Boll et al. compared multimedia document models according to advanced re-
quirements for reusability, adaptability and usability from a technical perspective. Im-
portant aspects regarding reusability are: (1) Granularity of media elements, fragments
and documents (2) Kind of reuse, i.e. structural or identical, and (3) Identification and
selection. Based on an assessment of different models according to these characteris-
tics we selected the MPEG-21 Digital Item Declaration (DID) Abstract Model [4] as a
data model that fulfills the basic characteristics of an adaptable data model, i.e. granular
description of fragments, media elements (resources), grouped resources (i.e. compo-
nents) and identification and selection of (parts of) resources. The basic parts of an
MPEG-21 Digital Item which are interesting from this aspect are depicted in Figure 1:
These include most notably containers in which identifiable digital assets are included
(items) and which contain (multimedia) resources. Fragments of these resources can
be selected and both resources and their fragments can be described via descriptors or
annotated via annotations. Our proposed data model is realized by an ontology which
covers the MPEG-21 DID Abstract Model and which amongst others makes the se-
mantic types of relations between media elements, components or fragments and their
descriptors explicit. The ontology is briefly described in section 4.3. The MPEG-21
DID Abstract Model is amongst other compatible with the OAI Abstract Information
Model as shown in [2] and our first investigations also indicate that it is compatible to
existing learning content models.
12
Exploiting Social and Content Related Features of Multimedia Content on the SW
Fig. 1. MPEG-21: Main elements within the Digital Item Declaration Model
4.2 A Metadata Model to Increase the Findability Rate of Content
Multimedia retrieval is the discipline of applying information retrieval techniques to
non-text- based content. The critical point for these techniques is, that users search for
non-text based content based on the aboutness of the contained information [13] which
is – if at all – represented by tags attached to the content or by information derived
from the deployment context of the content which – when used for search – is blurred
in the retrieved result sets. This is why reliable metadata is often essential to enable
retrieval of multimedia content. It is commonly acknowledged that a metadata model
which increases the findability rate of content also increases the reusability potential of
content [18]. We follow a two-fold strategy to increase the findability rate of content:
(1) First descriptions provided along with the content on web sites are explicitly related
with the content to provide hints for search engines where to find information that can
be used for indexing. This is supported by the data model introduced in section 4.1. (2)
Secondly, content will be accompanied by metadata sets following a metadata model
which captures relevant social and content related features and which is outlined in the
subsequent section.
Social Aspects: The Role of Different Users in Content and Metadata Production
We analysed the life cycle of both content and metadata and the roles of distinct user
groups in order to determine components that our metadata model has to provide. Here,
we especially took social aspects of content into account which is an important indicator
for reuse. The content lifecycle consists of the following different dimensions (cf. [14]):
1. The User Dimension: Content and metadata is produced, altered and consumed by
different users playing different roles: Production related users who create, process,
resell, or publish content and end users who mainly consume but also increasingly
produce content.
2. The Content Dimension: During its lifetime content is transferred between dif-
ferent stages. According to the canonical processes of media production [10] it is
premediated, created (processed), annotated, packaged, organized and distributed.
3. The Metadata Dimension: Metadata is potentially being added by different users
in every step of the content lifecycle. In [9] four different metadata creation roles
13
Exploiting Social and Content Related Features of Multimedia Content on the SW
are introduced: The content creator who directly provides metadata, professional
metadata creators who get paid for annotating content, technical metadata creators
who just add basic technical metadata, and community enthusiasts which are very
prominent in the Web 2.0 and tag content.
From this observation we are able to derive that metadata about content is not static
and should be changeable during lifetime. Furthermore metadata is potentially being
provided by different parties.
Unblurring Content Descriptions: Supporting Multiple Metadata Sets In the liter-
ature the distinction is made between authorative and non-authorative metadata. Au-
thorative metadata is contributed by the author (creator) of the content and reflects
persistent information about the content. Non-authorative metadata is provided by the
consumer or a third party and provides contextual and changing aspects [17]. Non-
authorative metadata is especially useful for recommendations based on collaborative
filtering techniques and thus is critical in the effective discovery and reuse of content. To
reflect different opinions and interpretations of content, metadata provided by different
parties should be connected to its originator which is an important indication of its qual-
ity and trustworthiness and thus should be kept separate. Our model therefore explicitly
supports one authorative metadata and multiple non-authorative sets to be attached to
different parts of the data model. While authorative metadata is explicitly added, non-
authorative metadata may explicitly be provided (through annotations, reviews, ratings,
etc.) or implicitly be generated (through harvesting or usage analysis).
Types of Metadata As previously said, metadata is critical for the discovery of non-
text based content on the Web. Metadata standards or vocabularies for multimedia are
yet numerous as we summarize in [12].
Our investigations of standards and types of metadata focused on a core set which
reflects the properties of content in its lifecycle. This set mostly covers the core facets
which are used across a variety of domains and which we believe are important to
support findability with respect to reuse:
1. Bibliographic metadata is traditionally concerned and related to the authorship of
content and includes basic fields like identification, naming, publication or catego-
rization.
2. Technical metadata typically describes physical properties of content, like format,
bit-rate and what is mostly called low-level features of content.
3. Classification metadata might include keywords or tags but also domain specific
classification information
4. Evaluative metadata includes ratings and qualitative assessments of the content.
A collaborative evaluation model based on evaluative metadata could provide in-
valuable information regarding reuse of content. This could include dimensions like
usefulness, presentation aesthetics, or design.
5. Relational metadata is one of the most important parts to be able to explicitly de-
fine relations between the content and other related information / content. Relations
can include explicit ones which are given through the design of the content object
14
Exploiting Social and Content Related Features of Multimedia Content on the SW
or external objects. However it might also contain implicit relations gathered by
observations or the usage history.
6. Rights metadata are of utmost importance with respect to reusability as they de-
clare the terms of use.
7. Functional metadata: Functions may be supported to alter the presentation of the
content, to customize or personalize the content, or to provide access to different
versions.
The selection of the different metadata types with respect to reuse was based on a set
of expert interviews and is currently empirically validated in a survey. The assignment
of metadata to resources is not restricted to the above mentioned types but is open to
domain-specific assignments like educational value for a learning object or preservation
data for archival information. In this respect our approach is inline with the vision of
Resource Profiles as described in [8].
4.3 Ontology Framework
The data and metadata model as explained in the previous sections are implemented
using a set of ontologies in order to publish and describe ICOs on the Web. The import-
graph of the ontologies used is depicted in Figure 22 : The main ontology is the RICO
Fig. 2. The Reusable Intelligent Content Objects (RICO) ontology
(“Reusable Intelligent Content Objects”) - ontology which imports a set of other on-
tologies. RICO is an OWL-DL ontology. Most notably it makes use of
– the MPEG-21 DIDL ontology which we built to reflect the data model presented in
section 4.1,
– the Mindswap Digital Media Ontology which is used to type resources3
– an OWL-DL version of the FOAF ontology as provided by Mindswap4 ,
2
The dark grey and white ontologies were built in the course of this work.
3
The Digital Media Ontology available at http://www.mindswap.org/2005/owl/
digital-media has been slightly adapted to be in OWL-DL.
4
http://www.mindswap.org/2003/owl/foaf
15
Exploiting Social and Content Related Features of Multimedia Content on the SW
– the Annotea annotations ontology to represent annotations5 ,
– the OWL-Lite version of the Dublin Core ontology, and
– the MARCO (“Metadata for Reusable intelligent Content”) - ontology which is
currently work in progress and which will cover aspects of the metadata model as
presented in section 4.2.
5 Deployment of Intelligent Content Objects on the Web using
RDFa
ICOs are published as compound objects on the (Semantic) Web following the data
model described in section 4.1, including metadata as described in section 4.2 and
marked up with RDFa using the ontologies as described in section 4.3. An ICO includes
the resource whose structure is described using the data model and multiple metadata
records including the metadata types previously presented.
The compound package information is about to be published inline within an HTML
page and will extend the ramm.x (“RDFa based multimedia metadata”) model that we
suggested in [11] and which can be used to deploy multimedia metadata using RDFa.
Fig. 3. A image hosted by Flickr deployed as an Intelligent Content package
5
The Annotea annotations ontology available at http://www.w3.org/2000/10/
annotation-ns# has been rebuilt in OWL-DL.
16
Exploiting Social and Content Related Features of Multimedia Content on the SW
Figure 3 shows an example of an ICO, i.e. an image hosted by Flickr for which all
descriptions are explicitly marked up and related to the image. The ICO contains three
metadata sets: one authorative set (as provided by the owner/creator of the image) and
two non-authorative sets (one provided by the hosting platform and one provided by
an end user through a commentary). The figure shows only how visible information
is related to the image. However further additional (non-visible) metadata could also
be provided, e.g. by providing a detailed description of different scenes in a video or
further semantic descriptions of the content of an image. Parts of the resulting RDF
graph are visualized in Figure 4 which is however not showing the entire graph because
of space restrictions (i.e. most descriptors are omitted).
Fig. 4. Partial description of the compound object from Figure 3.
6 Related Work
The model and the ontologies can be used to markup illustrative images in typical web
pages, images or videos hosted by social media sharing sites, slides and images embed-
ded in commercial offerings, or multimedia content deployed in blogs and wikis.
Especially in recent years much work has been done on the specification of ontologies
that aim to combine traditional multimedia description models, thus trying to develop
models that allow reasoning over the structure and semantics of multimedia data (see
[5] and [12] for a comprehensive overview). Models with a similar intention, i.e. to pub-
lish semantic metadata inline of HTML pages, include lightweight approaches like the
hMedia microformat6 which is a basic vocabulary to mark up media resources on web
sites using property value pairs. Furthermore it is related to the ramm.x model which
provides a small but extensible vocabulary for marking up resources to include legacy
6
http://wiki.digitalbazaar.com/en/Media\_Info\_Microformat
17
Exploiting Social and Content Related Features of Multimedia Content on the SW
metadata. Ramm.x however does not include something similar to our data model or
a detailed metadata model. The intention of the SMIL MetaInformation-Module7 is to
publish RDF-based metadata in SMIL presentations. It is very general and does not
prescribe how to use it. We intend to test the applicability of our proposed model with
SMIL in the future. The intention of our model is also similar to Adobe’s XMP whose
intention is to publish RDF-based metadata into PDFs or other document formats. Fur-
thermore we want to acknowledge the work being done by Creative Commons to de-
scribe and embed licensing data using RDF which is exploited in searches by Yahoo or
in Flickr8 .
More heavyweight approaches include Intelligent Content models as previously as-
sessed for example in [6] and which cover a broad range of aspects. Most of these
approaches are too heavy for our proposed model.
Traditional models include the standardized framework of MPEG-7 [15] or packag-
ing formats from the archival or E-Learning domain which include the IMS Content
Packaging format9 , the Metadata Encoding and Transmission Standard (METS)10 , the
MPEG-21 Digital Item Declaration (DID) [4] and most recently the OAI-Object Reuse
and Exchange - model (OAI-ORE)11 . The OAI-ORE model has been designed as an
exchange format for scholarly works. Its compound objects model12 [21] is similar to
our model as it also provides facilities to publish semantic descriptions as an overlay
graph over web pages. The approach however does not focus on multimedial aspects.
The intention of the presented model is not provide a new standard for the descrip-
tion of the semantic content and content decomposition like it is done by MPEG-7. Thus
our approach is only marginally related to endeavors that aim to combine MPEG-7 with
semantic technologies like the COMM ontology or other available MPEG-7 ontologies
(see cf. [20] for a comparison).
7 Conclusions and Future Work
In this paper we presented a model for deploying multimedia content descriptions, i.e.
Intelligent Content Objects, on the Semantic Web with the goal to increase the reusabil-
ity potential of content in general. The model consists of a data model that supports
adaptability of content, a metadata model including properties to explicitly increase
findability with respect to reuse (e.g. implicit usage information, ratings, etc.) and a
deployment facility to publish content descriptions inline of HTML pages. Deploying
resources using this model can have a similar effect like Yahoo’s Search Monkey13
which allows people to mark up their content using Microformats or RDFa whereas the
additional information is then used to display and probably rank search results.
7
http://www.w3.org/TR/2007/WD-SMIL3-20070713/smil-metadata.html
8
http://search.creativecommons.org/
9
http://www.imsglobal.org/content/packaging/
10
http://www.loc.gov/standards/mets/
11
http://www.openarchives.org/ore/
12
http://www.openarchives.org/ore/documents/
CompoundObjects-200705.html
13
http://developer.yahoo.com/searchmonkey/
18
Exploiting Social and Content Related Features of Multimedia Content on the SW
Future work includes the engineering of the MARCO ontology and a qualitative
evaluation of our approach. The evaluation of the parts of the model is work in progress.
The basic model, which includes the data model and the metadata categorization, meets
the requirements of a typical multimedia publishing scenario on the Web and fulfills
the criteria of an adaptable data model as defined in [3]. However the effect of the
different metadata types on the reuse of content has yet to be validated. We are currently
empirically validating the influence of different metadata types on the reuse of content
in a study which will be accompanied by an implementation. Different aspect that would
also demand special attention but which are beyond the scope of our work include the
assessment of the heterogeneity or the consistency of different metadata sets.
References
1. Wernher Behrendt, Guntram Geser, and Andrea Mulrenin. Ep2010 - the future of electronic
publishing towards 2010. Information Society DG - Unit E2, EUFO 1-275, Rue Alcide de
Gasperi, L-2920, Luxembourg, 2003.
2. Jeroen Bekaert, Emiel De Kooning, and Herbert van de Sompel. Representing digital as-
sets using mpeg-21 digital item declaration. International Journal on Digital Libraries,
6(2):159– 173, 2006.
3. Susanne Boll and Woflgang Klas. Zyx – a multimedia document model for reuse and adap-
tation of multimedia content. IEEE Transactions on Knowledge and Data Engineering,
13(3):361–382, 2001.
4. Jan Bormans and Keith Hill. Mpeg-21 overview v.5, 2002. ISO/IEC JTC1/SC29/WG11.
5. Tobias Bürger and Michael Hausenblas. Why real-world multimedia assets fail to enter
the semantic web. In Proc. of the Int. Workshop on Semantic Authoring, Annotation and
Knowledge Markup (SAAKM07), 2007.
6. Tobias Bürger, Ioan Toma, Omair Shafiq, and Daniel Dögl. State of the art in sws, grid
computing and intelligent content objects - can they meet? GRISINO Deliverable D1.1,
http://www.grisino.at, October 2006. GRISINO Deliverable D1.1.
7. CCSDS. Reference model for an open archival information system. Blue Book 1, Consul-
tative Committee for Space Data Systems, CCSDS Secretariat Program Integration Division
(Code M-3) National Aeronautics and Space Administration Washington, DC 20546, USA,
Januar 2002.
8. Stephen Downes. Resource profiles. Journal of Interactive Media in Education Special Issue
on the Educational Semantic Web, 5:0, 2004.
9. Jane Greenberg. Metadata generation: Processes, people, and tools. Bulletin of American
Society for Information Science and Technology, 29(2), 2003.
10. Lynda Hardman. Canonical processes of media production. In MHC ’05: Proceedings of the
ACM workshop on Multimedia for human communication, pages 1–6, New York, NY, USA,
2005. ACM Press.
11. Michael Hausenblas, Werner Bailer, Tobias Bürger, and Raphael Troncy. Ramm.x: Deploy-
ing multimedia metadata on the semantic web. In Proceedings of SAMT 2007, Dec 4-7,
Genova, Italy, 2007.
12. Michael Hausenblas, Susanne Boll, Tobias Bürger, Oscar Celma, Christian Halaschek-
Wiener, Erik Mannens, and Raphael Troncy. Multimedia vocabularies on the semantic web.
W3c incubator group report, W3C, July 2007.
13. Laura Hollink, Guus Schreiber, Bob Wielinga, and Marcel Worring. Classification of user
image descriptions. Int. J. Hum.-Comput. Stud., 61(5):601–626, 2004.
19
Exploiting Social and Content Related Features of Multimedia Content on the SW
14. Harald Kosch, Laszlo Boszormenyi, Mario Doller, Mulugeta Libsie, Peter Schojer, and An-
drea Kofler. The life cycle of multimedia metadata. IEEE MultiMedia, 12(1):80–86, 2005.
15. José M. Martı́nez-Sanchez, Rob Koenen, and Fernando Pereira. Mpeg-7: The generic multi-
media content description standard, part 1. IEEE MultiMedia, 9(2):78–87, 2002.
16. Frank Nack, Jacco van Ossenbruggen, and Lynda Hardman. That Obscure Object of Desire:
Multimedia Metadata on the Web (Part II). IEEE Multimedia, 12(1), 2005.
17. Mimi Recker and David Wiley. A non-authoritative educational metadata ontology for fil-
tering and recommending learning objects. Journal of Interactive Learning Environments,
2001.
18. Ann Rockley. Managing Enterprise Content: A Unified Content Strategy. New Riders, 2002.
19. Arnold W. M. Smeulders, Marcel Worring, Simone Santini, Amarnath Gupta, and Ramesh
Jain. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal.
Mach. Intell., 22(12):1349–1380, 2000.
20. Raphael Troncy, Oscar Celma, Suzanne Little, Roberto Garcia, and Chrisa Tsinaraki. Mpeg-
7 based multimedia ontologies: Interoperability support or interoperability issue? In Pro-
ceedings of the 1st Workshop on Multimedia Annotation and Retrieval enabled by Shared
Ontologies (Mareso ’07), 2007.
21. Herbert van de Sompel and Carl Lagoze. Interoperability for the discovery, use, and re-use
of units of scholarly communication. CTWatch Quarterly, 3(3), August 2007.
22. Jacco van Ossenbruggen, Frank Nack, and Lynda Hardman. That Obscure Object of Desire:
Multimedia Metadata on the Web (Part I). IEEE Multimedia, 11(4), 2004.
20