Ontology Based Content Management for Digital TV

Services

Benjamin LuiChinese University of Hong

Kong benjamin_lui@hotmail.com

Dickson K. W. CHIUSenior Member, IEEE

Dickson Computer SystemsHong Kong

dicksonchiu@ieee.org

Haiyang Hu, Hua Hu, Yi Zhuang

Zhejiang Gongshang University, China{hhy, huhua, zhuang}@mail.zjgsu.edu.cn

Ontology for DTV

CMS SOKM 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

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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

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Simplified Ontology forContent Annotation

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Partial Ontology Listing for Multimedia Content annotation

<owl:Ontology rdf:about="#Content"> <rdfs:comment>Simplified Content Ontology</rdfs:comment> <owl:Class rdf:ID="Content" /> <owl:Class rdf:ID="Genre" /> <owl:Class rdf:ID="Category"> <rdfs:subClassOf rdf:resource="#Genre" /> ... </owl:Class> <owl:ObjectProperty rdf:ID="hasGenre"> <rdfs:domain rdf:resource="#Content" /> <rdfs:range rdf:resource="#Genre" /> </owl:ObjectProperty> ... <owl:DatatypeProperty rdf:ID="class"> <!-- Enumeration --!> <rdfs:domain rdf:resource="#Genre"/> <rdfs:range> <owl:DataRange> <owl:oneOf> <rdf:List> <rdf:rest> <rdf:List> <rdf:rest><rdf:List> <rdf:rest><rdf:List> <rdf:rest rdf:resource="http://www.w3.org/1999/02/22-rdf-syntax-ns#nil"/> <rdf:first rdf:datatype="http://www.w3.org/2001/XMLSchema#string">I</rdf:first></rdf:List></rdf:rest>…<rdf:first rdf:datatype="http://www.w3.org/2001/XMLSchema#string">III</rdf:first></rdf:List> </owl:oneOf></owl:DataRange></rdfs:range> </owl:DatatypeProperty> …</owl:Ontology>

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Semantic basede-Marketplace Conceptual Model

concert =>

music & performance

fighting vs. Kung-fu

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Process Model of the CMS

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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.

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System Implementation Architecture

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Advantages of Using Ontology

Traditional CMS Contributions 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

Ontology for DTV

CMS SOKM 2009- 11

Advantages of Using Ontology (cont)

Traditional CMS Contributions 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)

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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

Ontology for DTV

CMS SOKM 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