Fuzzy logic and hybrid approaches to Web intelligence gathering and information management

The advent of the World Wide Web during the last decade has brought unique challenges for organizations across the globe and inspired them to adapt to a new order of information distribution. The availability and accessibility of various types of mission critical information has transformed many of the basic principles of business processes. To effectively implement the broad range spectrum of activities ranging from supply chain reconnaissance to reputation management, an increasing number of organizations are deploying intelligence gathering systems in one form or another. As a result, new information technologies are in demand because the information available on the Web is unstructured, hard to find, and highly dynamic. In addition, because the information is usually contained in natural language formats, such as essays, news articles, or analysts' reports, the flexible, vague and imprecise character of natural language imposes further challenges on intelligence gathering. Among the new technologies developed to address these problems, fuzzy logic and fuzzy rule based systems have gained increasing importance due to their undisputed advantage in handling vague and imprecise information. This paper focuses on fuzzy logic applications and research topics in the realm of Web intelligence gathering and information management. The three fundamental stages of (1) retrieval, (2) analysis, and (3) storage are examined and fuzzy logic related research and development (R and D) areas are presented. The paper also identifies the general trend in R and D that responds to the market demand, and concludes with a brief look at the importance of hybrid computational intelligence approaches that involve several other disciplines, such as ontological semantics, computational linguistics, and neural networks.

[1]  Hidetomo Ichihashi,et al.  Collaborative Filtering Using Principal Component Analysis and Fuzzy Clustering , 2001, Web Intelligence.

[2]  Lotfi A. Zadeh,et al.  The Roles of Fuzzy Logic and Soft Computing in the Conception, Design and Deployment of Intelligent Systems , 1997, Software Agents and Soft Computing.

[3]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[4]  Geert Wets,et al.  Reducing redundancy in characteristic rule discovery by using IP-techniques , 1999 .

[5]  David B. Fogel.  An introduction to genetic algorithms: Melanie Mitchell. MIT Press, Cambridge MA, 1996. $30.00 (cloth), 270 pp , 1997 .

[6]  Victor Raskin,et al.  Ontology, sublanguage, and semantic networks in natural language processing , 1990 .

[7]  Joseph P. Bigus,et al.  Data mining with neural networks , 1996 .

[8]  Jiawei Han,et al.  Discovering Web access patterns and trends by applying OLAP and data mining technology on Web logs , 1998, Proceedings IEEE International Forum on Research and Technology Advances in Digital Libraries -ADL'98-.

[9]  Carol L. Barry.  User-defined relevance criteria: an exploratory study , 1994 .

[10]  Vipin Kumar,et al.  Partitioning-based clustering for Web document categorization , 1999, Decis. Support Syst..

[11]  Anupam Joshi,et al.  Robust Fuzzy Clustering Methods to Support Web Mining , 1998 .

[12]  Paul Solomon,et al.  Toward an Understanding of the Dynamics of Relevance Judgment: An Analysis of One Person's Search Behavior , 1998, Inf. Process. Manag..

[13]  Da Ruan.  Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms , 1997 .

[14]  Witold Pedrycz,et al.  Data Mining Methods for Knowledge Discovery , 1998, IEEE Trans. Neural Networks.

[15]  James D. Thomas.  Integrating Genetic Algorithms and Text Learning for Financial Prediction , 2000 .

[16]  Vasant Dhar,et al.  Seven Methods for Transforming Corporate Data Into Business Intelligence , 1996 .