Knowledge-Driven Video Information Retrieval with LOD

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Transcription:

Knowledge-Driven Video Information Retrieval with LOD Leslie F. Sikos, Ph.D., Flinders University ESAIR 15, 23 October 2015 Melbourne, VIC, Australia

Knowledge-Driven Video IR Outline Video Retrieval Challenges and Limitations Unstructured vs. Structured Annotations Bridging the Semantic Gap Multimedia Vocabularies and Ontologies Linked (Open) Data for multimedia Standardization of Video Annotations

Unstructured vs. Structured Data Plain text tags XML/XSD metadata RDFS/OWL annotations

Vocabularies and Ontologies Dublin Core Creative Commons Schema.org Concept definitions Classes, properties, individuals Relationships Rules

Video Object Representation Lightweight Annotations HTML5 Microdata JSON-LD RDFa Microformats

Video Object Representation Lightweight Annotations

Video Object Representation Lightweight Annotations

Video Object Representation Semi-Structured Vocabularies Vocabulary Standard Language MPEG-7 ISO/IEC 15938 XSD MPEG-21 ISO/IEC 21000 XSD NewsML IPTC NewsML-G2 XSD TV-Anytime ETSI TS 102 822 XSD

Video Object Representation Semi-Structured Vocabularies Vocabulary Standard Language MPEG-7 ISO/IEC 15938 XSD MPEG-21 ISO/IEC 21000 XSD NewsML IPTC NewsML-G2 XSD TV-Anytime ETSI TS 102 822 XSD

XSD to RDFS/OWL Mapping MPEG-7 COMM MPEG-7Ontos SWIntO

Issues Inherited from MPEG-7 Strong focus on low-level descriptors color distribution feature values of an image for red, black, and yellow still do not allow the conclusion that the image shows a sunset Boll et al., 1998

Issues Inherited from MPEG-7

Issues Inherited from MPEG-7

Issues Inherited from MPEG-7

Issues Inherited from MPEG-7

Issues Inherited from MPEG-7 Strong focus on low-level descriptors Conceptual ambiguity Semantic interoperability issues Syntactic interoperability issues Structural complexity: 1,182 elements, 417 attributes, and 377 complex types

Custom Ontologies Large Scale Concept Ontology for Multimedia (LSCOM) Linked Movie Database Ontology Multimedia Metadata Ontology (M3O) Ontology for Media Resources

Custom Ontologies T TBox: terminological knowledge A ABox: assertional knowledge

Custom Ontologies T TBox: terminological knowledge A ABox: assertional knowledge Knowledge Base

Custom Ontologies T TBox: terminological knowledge A ABox: assertional knowledge R RBox: role inclusion axioms + transitivity axioms

Multimedia Ontologies Limited DL Expressivity Ontology Language DL LinkedMDB RDFS AL LSCOM OWL AL M3O OWL SH IQ ( D) COMM OWL SH OI N ( D)

Multimedia Ontologies Full DL Expressivity Ontology Language DL LinkedMDB Ontology Language RDFS AL DL LSCOM VidOnt OWL OWL 2 SR AL OI Q ( D) M3O OWL SH IQ ( D) http://vidont.org COMM OWL SH OI N ( D)

Domain Ontologies OWL 2 ontologies + SWRL rules Decidability

Domain Ontologies OWL 2 ontologies + SWRL rules Decidability Solution: OWL 2 + DL Rules + DL-safe rules: very expressive & still decidable

Linked Data for Multimedia Rationale Global identification with URIs Linking to annotations or media fragments in the Linked Data cloud Differentiate video objects and media fragments from information resources Support access through SPARQL queries

Video IR with LOD Conclusions OWL 2 ontologies are needed a) Alignment with standards Advanced b) Exploit SR OI Q ( D) constructs } inference & c) Define Rbox axioms reasoning d) Use OWL 2+DL-safe rules support LOD: video understanding, discovery,

Questions

Thank you. For more on semantic multimedia, visit www.lesliesikos.com