Factoid mining based content trust model for information retrieval

Authors:
Wei Wang;Guosun Zeng;Mingjun Sun;Huanan Gu;Quan Zhang
Affiliations:
Department of Computer Science and Technology, Tongji University, Shanghai, China and National Engineering & Technology Center of High Performance Computer, Shanghai, China;Department of Computer Science and Technology, Tongji University, Shanghai, China and National Engineering & Technology Center of High Performance Computer, Shanghai, China;Department of Computer Science and Technology, Tongji University, Shanghai, China and National Engineering & Technology Center of High Performance Computer, Shanghai, China;Department of Computer Science and Technology, Tongji University, Shanghai, China and National Engineering & Technology Center of High Performance Computer, Shanghai, China;Department of Computer Science and Technology, Tongji University, Shanghai, China and National Engineering & Technology Center of High Performance Computer, Shanghai, China
Venue:
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Year:
2007

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Abstract

Trust is an integral component in many kinds of human interactions and the need for trust spans all aspects of computer science. While most prior work focuses on entity-centered issues such as authentication and reputation, it does not model the information itself, which can be also regarded as quality of information. This paper discusses content trust as a factoid ranking problem. Factoid here refers to something which can reflect the truth of the content, such as the definition of one thing. We extracts factoid from documents' content and then rank them according to their likehood as a trustworthy ones. Learning methods for performing factoid ranking are proposed in this paper. Trust features for judging the trustworthiness of a factoid is given, and features for constructing the Ranking SVM models are defined. Experimental results indicate the usefulness of this approach.