1. Ontology Based Content Management for Digital TV Services Benjamin Lui Chinese University of Hong Kong benjamin_lui@hotmail.com Dickson K. W. CHIU Senior Member, IEEE Dickson Computer Systems Hong Kong dicksonchiu@ieee.org Haiyang Hu, Hua Hu, Yi Zhuang Zhejiang Gongshang University, China {hhy, huhua, zhuang}@mail.zjgsu.edu.cn

2. Ontology for DTV CMSSOKM 2009- 2 Introduction Content management systems (CMS) are widely used in various industries. Semantic gap – discrepancy between the way video contents are coded digitally and the way they are experienced by human users. General Problems: classification => searching DTV problems Subscribe channels through set-top boxes Single direction Limited ability in providing recommendations

3. Ontology for DTV CMSSOKM 2009- 3 Agent-based Solution Overview Proactively search channels or VOD contents for users based on different categories Recommend channels based on channel classifications, user subscription, and profile preferences for up-sale Notify users of new interesting contents Provide statistical reports of users subscription in both vertical and horizontal domains service provider can review the business needs and then look for new potential contents *** Use ontology

4. Ontology for DTV CMSSOKM 2009- 4 Simplified Ontology for Content Annotation

5. Ontology for DTV CMSSOKM 2009- 5 Partial Ontology Listing for Multimedia Content annotation Simplified Content Ontology...... I … III …

6. Ontology for DTV CMSSOKM 2009- 6 Semantic based e-Marketplace Conceptual Model concert => music & performance fighting vs. Kung-fu

7. Ontology for DTV CMSSOKM 2009- 7 Process Model of the CMS

8. Ontology for DTV CMSSOKM 2009- 8 Understanding Requirements from Ontologies Match key requirement issues like class, category, language as genre For each issue, check if a direct mapping to a genre based on an agreed ontology is possible. If not, the agent tries to see if sets of relating genres can be mapped by well-known graph search algorithms. If the user accepts the new genre, new contents are recommended. Further the agent refines the issue with new search criteria. Another set of related genres can be extracted. Eventually more and more content matched users’ interests can be provided.

9. Ontology for DTV CMSSOKM 2009- 9 System Implementation Architecture

10. Ontology for DTV CMSSOKM 2009- 10 Advantages of Using Ontology Traditional CMSContributions of Ontology Match- making Match-making often ineffective because of rigid definition of contents (e.g., categories) predefined by service providers Shared and agreed ontology provides common, flexible, and extensible definitions of multimedia contents for match- making and subsequent business processes Difficult to specify unclear types of multimedia content out of predefined categories Complicated use requirements can be decomposed into simple genres for elicitation of options

11. Ontology for DTV CMSSOKM 2009- 11 Advantages of Using Ontology (cont) Traditional CMSContributions of Ontology Recom- mendation Often only possible within the same category Ontology helps elicit alternatives for recommendation Pre-defined formulae for every type of multimedia contents are needed for evaluation Ontology help recommendation by evaluating offers in terms of flexible overall scaling Business analysis Simple data mining: mainly depends on viewership of single channel / program Ontology help analyze the viewership of related channels and programs to achieve a better marketing strategy ( Segmentation of contents and users)

12. Ontology for DTV CMSSOKM 2009- 12 Conclusions  Ontology applied to an agent-based CMS system of DTV service.  Conceptual model for the content searching and subscription  Ontology helps improve mutual understanding between users and service providers  Relationship of contents can be extracted effectively for cross-sale and business analysis

13. Ontology for DTV CMSSOKM 2009- 13 Future Work  Formal models  Elicitation of semantic distances  Enhancement of ontology-based matchmaking and recommendation algorithms  Ontology-based cross-sale and up-sale  Mobile clients and location / context-sensitive recommendations