Intelligent Exploration of the Web

1. Front Matter

   Pages I-XII

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2. Creation and Representation of Web Resources

   1. Front Matter

      Pages 1-1

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   2. Structure Analysis and Generation for Internet Documents

      • Kyong Ho Lee, Yoon Chul Choy, Sung-Bae Cho

      Pages 3-18

   3. A Fuzzy System for the Web Page Representation

      • Angela Ribeiro, Víctor Fresno, María C. Garcia-Alegre, Domingo Guinea

      Pages 19-37

   4. Flexible Representation and Retrieval of WEB Documents

      • Gloria Bordogna, Gabriella Pasi

      Pages 38-53

3. Information Retrieval

   1. Front Matter

      Pages 55-55

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   2. Intelligent Information Retrieval on the Web

      • Mieczysław Kłopotek

      Pages 57-73

   3. Internet as a Challenge to Fuzzy Querying

      • Janusz Kacprzyk, Sławomir Zadrożny

      Pages 74-95

   4. Internet Search Based on Text Intuitionistic Fuzzy Similarity

      • P. S. Szczepaniak, A. Niewiadomski

      Pages 96-102

   5. Content-Based Fuzzy Search in a Multimedia Web Database

      • Marina Teresa Pires Vieira, Mauro Biajiz, Sérgio Ricardo Borges Júnior, Eduardo Cotrin Teixeira, Fernando Genta dos Santos, Josiel Maimoni Figueiredo

      Pages 103-118

   6. Self-Organizing Maps for Interactive Search in Document Databases

      • Andreas Nürnberger, Aljoscha Klose, Rudolf Kruse

      Pages 119-135

   7. Methods for Exploratory Cluster Analysis

      • Samuel Kaski, Janne Nikkilä, Teuvo Kohonen

      Pages 136-151

   8. Textual Information Retrieval with User Profiles Using Fuzzy Clustering and Inferencing

      • Donald H. Kraft, Jianhua Chen, Maria J. Martin-Bautista, Maria-Amparo Vila

      Pages 152-165

   9. Intelligent Clustering as Source of Knowledge for Web Dialogue Manager in a Information Retrieval System

      • Paulo Quaresma, Irene Pimenta Rodrigues

      Pages 166-180

   10. Documents Clustering Using Tolerance Rough Set Model and Its Application to Information Retrieval

       • Tu Bao Ho, Saori Kawasaki, Ngoc Binh Nguyen

       Pages 181-196

   11. Improving Web Search by the Identification of Contextual Information

       • Fernando Aguiar

       Pages 197-224

   12. Intelligent Internet-Based Multiagent Systems

       • Brent Blackstock

       Pages 225-239

   13. Neural Agent for Text Database Discovery

       • Yong S. Choi

       Pages 240-254

   14. Intelligent Web Agents that Learn to Retrieve and Extract Information

       • Tina Eliassi-Rad, Jude Shavlik

       Pages 255-274

   15. Accurately and Reliably Extracting Data from the Web: A Machine Learning Approach

       • Craig A. Knoblock, Kristina Lerman, Steven Minton, Ion Muslea

       Pages 275-287

The Web is the nervous system of information society. As such, it has a pervasive influence on our daily lives. And yet, in some ways the Web does not have a high MIQ (Machine IQ). What can be done to enhance it? This is the leitmotif of "Intelligent Exploration of the Web," (lEW)--a collection of articles co-edited by Drs. Szczepaniak, Segovia, Kacprzyk and, to a small degree, myself. The articles that comprise lEW address many basic problems ranging from structure analysis of Internet documents and Web dialogue management to intelligent Web agents for extraction of information, and bootstrapping an ontology-based information extraction system. Among the basic problems, one that stands out in importance is the problem of search. Existing search engines have many remarkable capabilities. But what is not among them is the deduction capability--the capability to answer a query by drawing on information which resides in various parts of the knowledge base. An example of a query might be "How many Ph.D. degrees in computer science were granted by European universities in 1996?" No existing search engine is capable of dealing with queries of comparable or even much lower complexity. Basically, what we would like to do is to add deduction capability to a search engine, with the aim of transforming it into a question-answering system, or a QI A system, for short. This is a problem that is of major importance and a challenge that is hard to meet.

• Web
• Web Databases
• World Wide Web
• classification
• data mining
• database
• expert system
• fuzzy system
• learning
• machine learning
• multimedia
• ontology
• organization
• proving
• web mining

• Institute of Computer Science, Technical University of Lodz, Lodz, Poland

  Piotr S. Szczepaniak

• Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

  Piotr S. Szczepaniak, Janusz Kacprzyk

• Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain

  Javier Segovia

• Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA

  Lotfi A. Zadeh

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