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Knowledge Mining

Proceedings of the NEMIS 2004 Final Conference

  • Spiros Sirmakessis

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 185)

Table of contents

  1. Front Matter
    Pages I-VIII
  2. Penelope Markellou, Maria Rigou, Spiros Sirmakessis
    Pages 1-11
  3. Simona Balbi, Michelangelo Misuraca
    Pages 23-29
  4. Sergio Bolasco, Alessio Canzonetti, Federico M. Capo, Francesca della Ratta-Rinaldi, Bhupesh K. Singh
    Pages 31-50
  5. Dimitrios Mavroeidis, George Tsatsaronis, Michalis Vazirgiannis
    Pages 93-107
  6. Georgia Panagopoulou
    Pages 109-122
  7. Federico Neri, Remo Raffaelli
    Pages 123-131
  8. Thierry Poibeau
    Pages 133-145
  9. Vangelis Karkaletsis, Constantine D. Spyropoulos
    Pages 147-157
  10. Mónica Bécue-Bertaut, Martin Rajman, Ludovic Lebart, Eric Gaussier
    Pages 159-179
  11. Alf Fyhrlund, Bert Fridlund, Bo Sundgren
    Pages 201-211
  12. Antoine Spinakis, Asanoula Chatzimakri
    Pages 223-232
  13. Bernd Drewes
    Pages 233-238
  14. Theoni Stathopoulou
    Pages 239-253
  15. Maria Teresa Pazienza, Marco Pennacchiotti, Fabio Massimo Zanzotto
    Pages 255-279
  16. Antoine Spinakis, Asanoula Chatzimakri
    Pages 281-290

About these proceedings

Introduction

Text mining is an exciting application field and an area of scientific research that is currently under rapid development. It uses techniques from well-established scientific fields (e.g. data mining, machine learning, information retrieval, natural language processing, case-based reasoning, statistics and knowledge management) in an effort to help people gain insight, understand and interpret large quantities of (usually) semi-structured and unstructured data. Despite the advances made during the last few years, many issues remain unresolved.

Knowledge Mining draws upon many of the key concepts of knowledge management, data mining and knowledge discovery, meta-analysis and data visualization. Within the context of scientific research, knowledge mining is principally concerned with the quantitative synthesis and visualization of research results and findings.

The book presents results from the application of knowledge mining techniques in various sector of the academic and indystrial research. The results are increased scientific understanding along with improvements in research quality and value. Knowledge mining products can be used to highlight research opportunities, assist with the presentation of "best" scientific evidence, facilitate research portfolio management, as well as, facilitate policy setting and decision making.

Keywords

Document Processing Knowledge Management Semantic Web Visualization Techniques Web Intelligence Web Mining algorithm algorithms calculus data analysis data mining modeling statistics visualization

Editors and affiliations

  • Spiros Sirmakessis
    • 1
  1. 1.Research Academic Computer TechnologyPatrasGreece

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-32394-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-25070-8
  • Online ISBN 978-3-540-32394-5
  • Series Print ISSN 1434-9922
  • Buy this book on publisher's site