Transcript Using Several Ontologies for Describing Audio-Visual Documents: A Case Study in the Medical Domain Antoine Isaac1 & Raphaël Troncy2 Sunday 29th of May, 2005 Multimedia and.

Using Several Ontologies for
Describing Audio-Visual
Documents:
A Case Study in the Medical Domain
Antoine Isaac1 & Raphaël Troncy2
Sunday 29th of May, 2005
Multimedia and the Semantic Web
Describe AV documents
• Various uses / Different granularities
– Identification, feature extraction, structural decomposition,
semantic description
• Description deep meaning cannot be accessed
and processed by systems
– Knowledge is often implicit: labels and comments in natural
language
• Formal semantics should be interesting
– Reasoning with AV document descriptions
– Interoperability with formal domain-specific ontologies, allowing
to mix AV and domain-related reasoning
 Use of Semantic Web technologies to better
retrieve, re-use and process AV content
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Objectives
• Settle an mini-experiment to show the benefits of
using semantic web technologies for annotating
multimedia content
• Show that the use of:
– formal ontologies and rules,
– inference capabilities,
– annotation design pattern
… are highly desirable for better accessing AV
content
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Agenda
• Corpus
• Ontological Resources
– AV Ontology
– Medical Ontology
•
•
•
•
Annotating the Videos
Querying the Knowledge Base
Performing Reasoning
Conclusion
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Corpus
• Medicine-related TV documentaries
– 30 documents, about 30 hours
– 50% deals with heart and heart surgery theme
• Good examples of how AV features are
used to popularize scientific notions
• Describe both the form and the content
– AV-oriented parts (documentary structure)
– Thematic-oriented parts (medicine notions)
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Ontological Resources
• Building an Audio-Visual Core Ontology [Isaac &
Troncy, 2004]
– Characterization of programs and sequences (AV genre)
– Decomposition of programs and sequences
– Ability to introduce description of the activities that constitute the
context of AV documents (roles of people involved, way
production and broadcast are achieved, etc.)
• Legitimacy
– Grounding conceptualization by observed purposes and domain
initiatives, study of 30 years of documentary practices
– Articulation with an upper-level ontology: DOLCE [Gangemi,
2002]
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Ontological Resources
<owl:Class rdf:ID="DialogSequence">
<rdfs:subClassOf rdf:resource="#SpokenSequence"/>
<rdfs:subClassOf>
<owl:Restriction>
<owl:onProperty>
<owl:ObjectProperty rdf:about="#hasParticipant"/>
</owl:onProperty>
<owl:minCardinality rdf:datatype="&xsd;int">2</owl:minCardinality>
</owl:Restriction>
</rdfs:subClassOf>
</owl:Class>
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Ontological Resources
• Extension of AV core with specific application
notions
– Exemplification, demonstration, etc.
• Re-use of Medical Ontologies
– Menelas: domain of coronary pathologies
• Concepts dealing with heart surgery
– Alternative choices are possible
• Galen (concepts dealing with surgical procedures)
• Articulation between the ontologies
– No use of automatic alignment methods or tools
– State by hand OWL axioms (equivalentClass)
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Description Process
• Segmentation of the AV material
– Selection of relevant documentary items
• Knowledge-based AV description
– Documentary structure characterization
– Segment description
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Segmenting the Videos
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Describing the Videos
• Annotation Mechanism
– The structure is described at the knowledge
level
• Concepts and relations from the AV ontology are
manually introduced in the description
– Content description
• Link to external world themes and entities
• Documentary patterns
– Layered approach [Troncy, 2003]
– AV description language [Troncy & Carrive, 2004]
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Describing the Videos
• Relational Indexing Pattern
– Help for user: specify how concepts and relations
have to be used
– Important for ontology conception and use (with
reasoning knowledge)
• Simple pattern that can lead to complex
descriptions
– Recursive relational structure
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Describing the Videos
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Querying the Knowledge Base
• Example:
« retrieve the programs that explain a disease and
show one of its causes »
• Need for the following inferences:
– Subsumption
CVDisease(x)  Disease(x)
– Composition
hasSubSequ ence(x,y)  explains(y, z)  explains(x, z)
hasSubSequ ence(x,y)  shows(y,z)  shows(x,z)
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Performing reasoning
• A layered complexity approach
– RDFS: subsumption
– OWL DL: complex definitions + algebraic properties
– Rules: horn clauses
• Concrete implementation
– RDFS: Sesame Architecture [Broekstra, 2002]
– OWL DL: BOR Reasoner [Simov, 2002]
– OWL-DLP [Grosof, 2003] + Rules : Sesame custom
inference module
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Examples
• DL definition
ExpertInterview  ( Interview
som e hasParticipant ( Person  som e role ExpertRole) )
ExpertRole  ( (academ icRole  professionalRole  hospitalRole)
 ( institutionRole) )
• Composition rule
hasSubSequ
ence ( x, y)  represents( y, z)  represents( x, z)
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Summary
Explicit
triples
Inferred
triples
RDF Model
All triples
129
AV Ontology
5231
10810
16041
Menelas
Ontology
10534
26637
37171
Instances
276
1507
1783
Total
16041
38954
54995
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Conclusion
• This experimentation:
– Uses Semantic Web languages and tools for describing AV
contents
– Uses several ontologies to capture both the structure and the
content of the documents
– Uses relational indexing patterns for the annotation
• Future work: thorough evaluation of those techniques
involving real users
• A problem that cannot be generally solved: fixing a tradeoff between expressivity and tractable computation
– Ad hoc, according to the needs of the application targeted
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