AVATAR: Advanced Telematic Search of Audivisual Contents by Semantic Reasoning Yolanda Blanco Fernández Department of Telematic Engineering University of Vigo (Spain) TV04: the 4th Workshop on Personalization in Future TV Eindhoven, The Netherlands August 23rd, 2004

Outline of this presentation TV recommender systems. –Motivation and previous approaches. Contribution of the AVATAR system. –Semantic reasoning. The AVATAR tool. –Main functionalities. TV-Anytime and ontologies. Conclusions and future work.

Motivation of TV Recommender Systems Migration from analogue to digital TV. Implications: –More channels in the same bandwidth. –Software applications mixed with audiovisual contents. Users need help to find interesting contents among irrelevant information.

Related Work Different approaches to recommend personal TV contents: –Naive Bayesian classifiers, decision trees, content- based techniques, collaborative filtering, etc. Several strategies are combined: –Higher quality and precission of the offered suggestions. Inference strategies with limited reasoning capabilities have been used in previous works.

The contribution of AVATAR The use of Semantic Web technologies to reason about the semantics of: –TV Contents –User Preferences –View History A personalized TV tool that offers enhanced recommendations beyond the syntactic content search.

Requirements of Semantic Reasoning The semantic reasoning process requires: –Descriptions of TV programs. –A knowledge representation mechanism that favour the reasoning and inference. For that purpose, our approach uses: –The TV-Anytime metadata. –Semantic Web technologies: a TV ontology.

The TV-Anytime initiative (I) TV-Anytime is a recent ETSI standard. 4 types of TV-Anytime metadata: – Content description metadata: Associate metadata with a piece of content (synopsis, genre, credits, awards, etc.) – Instance description metadata: Describe instances of contents (events in a service, program location, etc.)

The TV-Anytime initiative (II) – Consumer metadata: Information about users. History logs User preferences. – Segmentation metadata: TV contents divided in several segments. The AVATAR system is able to offer the most interesting part of a program.

A TV Ontology (I) The ontologies allow to share and reuse knowledge efficiently. Implementation of an ontology by means of the Protégé-2000 tool. –Classes related to the TV contents. –Properties describing their main characteristics TV-Anytime metadata. Ontology language: OWL (OWL DL)

A TV Ontology (II) Generation of properties from a database with different user profiles by means of a Naive Bayesian classifier. Properties of TV ontology: –Properties that relate the user personal data with TV programs Start semantic reasoning. –Properties that describe TV contents Continue semantic reasoning. Knowledge base of AVATAR classes, properties and specific individuals.

The AVATAR tool LEARNING CLUSTERING RECOMMENDATIONS SEMANTIC SELECTION RECOMMENDATIONS LEARNING USER PROFILE USER PROFILE TV CONTENTS TV CONTENTS SEMANTIC SELECTION USER PROFILE USER PROFILE

Conclusions Semantic Web technologies can be used in the context of TV. – Ontologies are useful for sharing and reusing knowledge. The semantic reasoning process enhances the offered recommendations. TV-Anytime is an appropriate initiative. – User preferences, history logs and TV contents descriptions.

Future Work OWL DL is an extended SHOQ DL we can use the DL reasoners: FACT and RACER. A query language (LIKO) to infer knowledge from the TV ontology. A RACER-based semantic matching algorithm to find TV contents that are semantically similar to the input TV program.

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