Mining concept associations for knowledge discovery through concept chain queries

Authors:
Wei Jin;Rohini K. Srihari;Xin Wu
Affiliations:
Department of Computer Science & Engineering, University at Buffalo, State University of New York;Department of Computer Science & Engineering, University at Buffalo, State University of New York;Department of Computer Science and Technology, University of Science and Technology of China, China
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

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Abstract

The availability of large volumes of text documents has created the potential of a vast amount of valuable information buried in those texts. This in turn has created the need for automated methods of discovering relevant information without having to read it all. This paper focuses on detecting links between two concepts across text documents. We interpret such a query as finding the most meaningful evidence trail across documents that connect these two concepts. In this paper we propose to use link-analysis techniques over the extracted features provided by Information Extraction Engine for finding new knowledge. We compare two approaches to perform this task. One is the concept-profile approach based on traditional bag-of-words model, and the other is the graph-based approach which combines text mining, graph mining and link analysis techniques. Counterterrorism corpus is used to evaluate the performance of each model and demonstrates that the graph-based approach is preferable for finding focused information. For greater coverage of information we should use the concept-profile based approach.