Supporting Literature Exploration with Granular Knowledge Structures

Authors:
Yiyu Yao;Yi Zeng;Ning Zhong
Affiliations:
Department of Computer Science, University of Regina, Regina, Saskatchewan S4S 0A2, Canada and International WIC Institute, Beijing University of Technology, Beijing, 100022, P.R. China;International WIC Institute, Beijing University of Technology, Beijing, 100022, P.R. China;International WIC Institute, Beijing University of Technology, Beijing, 100022, P.R. China and Department of Information Engineering, Maebashi Institute of Technology, Maebashi-City, 371-0816, Jap ...
Venue:
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

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Abstract

Reading and literature exploration are important tasks of scientific research. However, conventional retrieval systems provide limited support for these tasks by concentrating on identifying relevant materials. New generation systems should provide additional support functionality by focusing on analyzing and organizing the retrieved materials. A framework of literature exploration support systems is proposed. Techniques of granular computing are used to construct granular knowledge structures from the contents, structures, and usages of scientific documents. The granular knowledge structures provide a high level understanding of scientific literature and hints regarding what has been done and what needs to be done. As a demonstration, we examine granular knowledge structures obtained from an analysis of papers from two rough sets related conferences.