The Semantic Web enables machine interpretation of documents by marking content with semantic tags linked through a domain model or ontology. While natural language closely aligns with these conceptual models, allowing effective semantic processing of text, multimedia interpretation faces challenges due to the semantic gap between perceptual media features and conceptual knowledge. Addressing this requires extending domain knowledge to include perceptual models. The Multimedia Ontology Language (M-OWL) supports this by modeling observable media features causally and probabilistically, linking them to concepts with spatial-temporal relations and inheritance. M-OWL also facilitates abductive reasoning using Bayesian networks, enabling robust understanding despite uncertain media observations.
History
W3C forum has undertaken the initiative of standardizing the ontology representation for web-based applications. The Web Ontology Language (OWL), standardized in 2004 after maturing through XML(S), RDF(S) and DAML+OIL is a result of that effort. Ontology in OWL (and some of its predecessor languages) has been successfully used in establishing semantics of text in specific application contexts.
The concepts and properties in these traditional ontology languages are expressed as text, making an ontology readily usable for semantic analysis of textual documents. Semantic processing of media data calls for perceptual modeling of domain concepts with their media properties. M-OWL has been proposed as an ontology language that enables such perceptual modeling. While M-OWL is a syntactic extension of OWL, it uses a completely different semantics based on probabilistic causal model of the world.
Key features
Syntactically, MOWL is an extension of OWL. These extensions enable
- Definition of media properties following MPEG-7 media description model.
- Probabilistic association of media properties with the domain concepts.
- Formal semantics to the media properties to enable reasoning.
- Formal semantics for spatio-temporal relations across media objects and events.
MOWL is accompanied with reasoning tools that support
- Construction of model of observation for a concept in multimedia documents with expected media properties.
- Probabilistic (Bayesian) reasoning for concept recognition with the model of observation.
See also
Bibliography
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