Using Several Ontologies for Describing Audio-

Visual Documents:A Case Study in the Medical

Domain

Sunday 29th of May, 2005

Antoine Isaac1 & Raphaël Troncy2

Multimedia and the Semantic Web

2005/05/29 A. Isaac & R. Troncy - MSW'2005 2

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

– Composition

Disease(x)x)CVDisease(

z),explains(x z),explains(yy)ence(x,hasSubSequ z)shows(x, z)shows(y, y)ence(x,hasSubSequ

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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

• Composition rule

),(),(),( zxrepresentszyrepresentsyxencehasSubSequ

))(

)((

))(

(

nRoleinstitutio

lehospitalRoalRoleprofessionleacademicRoExpertRole

ExpertRolerolesomePersonpanthasParticisome

InterviewrviewExpertInte

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Summary

Explicit triples

Inferred triples

All triples

RDF Model 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 trade-off between expressivity and tractable computation– Ad hoc, according to the needs of the application targeted