=Paper=
{{Paper
|id=Vol-233/paper-1
|storemode=property
|title=Mind The Gap - Requirements for the Combination of Content and Knowledge
|pdfUrl=https://ceur-ws.org/Vol-233/p01.pdf
|volume=Vol-233
|dblpUrl=https://dblp.org/rec/conf/samt/BurgerW06
}}
==Mind The Gap - Requirements for the Combination of Content and Knowledge==
https://ceur-ws.org/Vol-233/p01.pdf
Mind the Gap - Requirements for the Combination
of Content and Knowledge
Tobias Bürger and Rupert Westenthaler
Abstract— In this short paper we report on a semantic model there were metadata models for content like MPEG-7 [3], and
for content and knowledge which distinguishes between three on the other hand there were domain ontologies developed by
descriptive levels: information relating directly to the resource, the Semantic Web community. However, in the past few years
to the meta data of the resource, and to the subject matter
addressed by the content. This model addresses five fundamental much work has been done on the specification of ontologies
requirements for automation: formality, interoperability, multiple that aim to combine traditional multimedia description models
interpretations, contextualisation, and independence of knowl- [4], [1]. Related approaches include work from two different
edge items from the resource’s content. research communities: First there are traditional content mod-
Index Terms— knowledge content objects, intelligent content els like MPEG-7 or MPEG-21 [5] which are coming from the
models, rich media content. multimedia community. Besides these traditional approaches
some efforts in modeling of intelligent content objects exist:
I. I NTRODUCTION More recent efforts include amongst others the ACEMEDIA3
ACE-objects [6] or the knowledge content objects (KCOs) of
Semantics – i.e. the interpretation of the content – is the METOKIS project4 .
important to make content machine-processable and to enable
the definition of tasks in workflow-environments for knowl-
III. R EQUIREMENTS FOR K NOWLEDGE C ONTENT
edge workers in the content industries. Some of the recent
research projects in the area of semantic (or symbolic) video Possible relations between knowledge and content are man-
annotation try to derive the semantics from the videos’ low ifold. Based on observed applications and requirements of
level features or from other available basic metadata. Most current projects (see [7] for details) we have derived the
of these approaches are – as also pointed out in [1] – not following requirements for knowledge content:
capable of fully exploiting the semantics of multimedia content 1) Knowledge must be encoded using a formal language
because the meaning of the content is not localized just in the 2) Interoperability especially for cross domain aspects
media that is being analysed. The construction of meaning is 3) Different interpretations of content objects
– for humans – an act of interpretation that has much more 4) Link content with knowledge that cannot be directly
to do with pre-existing knowledge and the context of the user derived from the content
and/or the media than with recognition of the contents’ low- 5) Make knowledge independent of content
level-features. This is known as the semantic gap [2]. Popular
examples on the Web show that there are currently many IV. KCO – A M ODEL FOR K NOWLEDGE C ONTENT
service-based platforms (like Flickr1 or LastFM2 ) that make
use of their users’ knowledge to understand the meaning of KCOs are based on the DOLCE foundational ontology5 and
multimedia content. have so-called semantic facets that form modular entities to
We suggest that representing richer semantics for multime- describe the properties of KCOs, including the raw content
dia content requires more expressive and more sophisticated object or media file, metadata and knowledge specific to the
knowledge content models than those currently used. We content object and knowledge about the topics of the content
therefore introduce a model for representing knowledge and (its meaning).
content alongside each other, with clear separation of the Knowledge is represented by the structure of the KCO in
content and the knowledge items, so as to obtain optimal con- three different levels:
ditions for content and knowledge reuse, and for subsequent 1) Resource Level: This level refers to the actual content
re-contextualization of content. object (File, stream, image, etc).
2) Meta Level: This level refers to knowledge describing
II. R ELATED W ORK features of the content object, eg. frame rate, compres-
sion type or colour coding scheme.
For a long time, combining knowledge and content did not 3) Subject Matter Level: This level comprises knowledge
play a great role in the research communities: On the one hand about the topic (subject) of the content as interpreted by
Tobias Bürger and Rupert Westenthaler are with Salzburg Research an actor. The content object realizes this interpretation.
Forschungsgesellschaft mbH, Salzburg, Austria. Contact: {tobias.buerger,
rupert.westenthaler}@salzburgresearch.at 3 http://www.acemedia.org
1 http://www.flickr.com 4 http://metokis.salzburgresearch.at
2 http://www.last.fm 5 http://www.loa-cnr.it/DOLCE.html
In addition to this knowledge structure the KCO also defines profiles containing additional knowledge about preferences of
a structure based on the different domains of the knowledge the users. This information can be used to further contextualize
objects. This structure is divided into six so-called facets, each queries by combining the context specified by the query, with
of them optimized for a specific usage. Facets include for the characteristics of the user profile. Based on the active
example a content- or community description facet [8]. concepts and relations of the contextualized query, the system
Relating the above description to the requirements from can find similar interpretations. Such a system is sensitive to
section III on knowledge content, we suggest that KCOs different interpretations of one and the same content object,
provide a good foundation for modeling combinations of because it handles different interpretations of different users
content and knowledge. separately.
1) Knowledge must be encoded using a formal lan- c) Context-based content classification to minimise the
guage: KCOs are based on the information objects semantic gap: This scenario refers to the problem of how
design pattern, which is an extension of the DOLCE to overcome the gap between low level features and higher
foundational ontology. The main concepts and relations level semantics. It assumes that new content objects typically
used in the description of the KCO are well grounded have to be analysed at the expense of some time and effort.
on this foundational framework. The current definition The complextity of this operation can be reduced based on
of KCOs is based on OWL-DL6 . background information about the content or some predefined
2) Interoperability especially for cross domain aspects: knowledge of parts of the content as knowledge about one part
The facet based structure of the KCO serves as a good can help to understand other parts of the content.
starting point for the alignment of standards to the KCO
structure. Some of the facets and elements there use parts VI. C ONCLUSIONS AND F UTURE W ORK
of different standards like NewsML7 or MPEG-7. More details about the reported work can be found in [7].
3) Different interpretations of content objects: The pos- We are currently trying to apply KCOs in several national and
sibility of different interpretations of content objects is international projects. Amongst them is the recently started
the main reason for distinguishing between the mesta IST project LIVE8 , in which we are responsible for the defini-
level and the subject matter level. Thus the KCO model tion of an intelligent media framework to support broadcasters
support multiple interpretations of one content object. in the live staging of media events.
4) Clear definition of possible relations between content
and knowledge: The KCO defines two different inter- VII. ACKNOWLEDGEMENTS
relations between content and knowledge: First, knowl-
edge objects can be about a content object, meaning The work reported here was part-funded by the EU projects
that the subject matter of the knowledge object is the METOKIS (contract number IST-FP6-507164) and LIVE
content object itself. Second, knowledge objects can be (contract number IST-FP6-27312).
realized by content objects. This relation is used for all
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