Automatic discovery of similarity relationships through Web mining

Authors:
Dmitri Roussinov;J. Leon Zhao
Affiliations:
School of Accountancy and Information Management, SAIM, College of Business, Arizona State University, Box 873606, Tempe, AZ;Department of MIS, School of Business and Public Administration, University of Arizona, Tucson, AZ
Venue:
Decision Support Systems - Web retrieval and mining
Year:
2003

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Abstract

This work demonstrates how the World Wide Web can be mined in a fully automated manner for discovering the semantic similarity relationships among the concepts surfaced during an electronic brainstorming session, and thus improving the accuracy of automated clustering meeting messages. Our novel Context Sensitive Similarity Discovery (CSSD) method takes advantage of the meeting context when selecting a subset of Web pages for data mining, and then conducts regular concept co-occurrence analysis within that subset. Our results have implications on reducing information overload in applications of text technologies such as email filtering, document retrieval, text summarization, and knowledge management.