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Multimedia Web Ontology Language (MOWL) has been designed to facilitate semantic interactions with multimedia contents. It supports perceptual modeling of concepts using expected media properties. While the reasoning in traditional ontology languages, e.g. Web Ontology Language (OWL), is based on Description Logics, MOWL supports a probabilistic reasoning framework based on Bayesian Network.

History Edit

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. Such modeling was first proposed in a Ph.D. Thesis by Hiranmay Ghosh (Electrical Engineering Department, IIT Delhi, 2002) in the form of Knowledge Description Language (KDL). With the standardization of OWL by W3C, KDL was merged with OWL to form Multimedia Web Ontology Language (MOWL).

Key Features Edit

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.

References Edit

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