Relevence Assessment of Topic Ontology

  • Authors:

  • Xujuan Zhou;Yuefeng Li;Yue Xu;Raymond Lau

  • Affiliations:

  • School of Software Engineering and Data Communications, Queensland University of Technology, Australia, x.zhou@student.qut.edu.au, {y2.li, yue.xu@qut.edu.au};School of Software Engineering and Data Communications, Queensland University of Technology, Australia, x.zhou@student.qut.edu.au, {y2.li, yue.xu@qut.edu.au};School of Software Engineering and Data Communications, Queensland University of Technology, Australia, x.zhou@student.qut.edu.au, {y2.li, yue.xu@qut.edu.au};Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, raylau@cityu.edu.hk

  • Venue:
  • Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
  • Year:
  • 2006

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Abstract

In traditional Information Retrieval (IR), user profiles are often represented by keyword/concepts space vectors or by some predefined categories. Unfortunately, this data is often inadequately or incompletely interpreted. Ontology-based user profile is another newer approach. This method is able to provide richer semantic information to facilitate information retrieval processes. It has become an important means for semantic-based information search and retrieval. Some ontology-based user profile models have been developed over the past few years. With the increasing usage of this method, it raises the issues of effective relevance measurement for the evaluation of ontologies. In practice, it is crucial to find a good relevance assessment algorithm for measuring the quality of ontologies. To represent user profile by relevant topic ontology, this paper presents a new method capable of measuring the user profile more objectively and hence has great potential to enhance the IR processes.