AVATAR: Advanced Telematic Search of Audivisual Contents by Semantic Reasoning

1. 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

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.)

8. 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.

9. 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)

10. 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.

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

12. 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.

13. Future Work • OWL DL is an extended SHOQDL  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.

14. Thank you