Knowledge Mining

1. Front Matter

   Pages I-VIII

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2. Knowledge Mining: A Quantitative Synthesis of Research Results and Findings

   • Penelope Markellou, Maria Rigou, Spiros Sirmakessis

   Pages 1-11

3. An Evidential Approach to Classification Combination for Text Categorisation

   • D. A. Bell, J. W. Guan, Y. X. Bi

   Pages 13-22

4. Visualization Techniques for Non Symmetrical Relations

   • Simona Balbi, Michelangelo Misuraca

   Pages 23-29

5. Understanding Text Mining: A Pragmatic Approach

   • Sergio Bolasco, Alessio Canzonetti, Federico M. Capo, Francesca della Ratta-Rinaldi, Bhupesh K. Singh

   Pages 31-50

6. Novel Approaches to Unsupervised Clustering Through k-Windows Algorithm

   • D. K. Tasoulis, M. N. Vrahatis

   Pages 51-77

7. Semiometric Approach, Qualitative Research and Text Mining Techniques for Modelling the Material Culture of Happiness

   • Furio Camillo, Melissa Tosi, Tiziana Traldi

   Pages 79-92

8. Semantic Distances for Sets of Senses and Applications in Word Sense Disambiguation

   • Dimitrios Mavroeidis, George Tsatsaronis, Michalis Vazirgiannis

   Pages 93-107

9. A Strategic Roadmap for Text Mining

   • Georgia Panagopoulou

   Pages 109-122

10. Text Mining Applied to Multilingual Corpora

    • Federico Neri, Remo Raffaelli

    Pages 123-131

11. Content Annotation for the Semantic Web

    • Thierry Poibeau

    Pages 133-145

12. An Open Platform for Collecting Domain Specific Web Pages and Extracting Information from Them

    • Vangelis Karkaletsis, Constantine D. Spyropoulos

    Pages 147-157

13. Extraction of the Useful Words from a Decisional Corpus. Contribution of Correspondence Analysis

    • Mónica Bécue-Bertaut, Martin Rajman, Ludovic Lebart, Eric Gaussier

    Pages 159-179

14. Collective SME Approach to Technology Watch and Competitive Intelligence: The Role of Intermediate Centers

    • Jorge Izquierdo, Sergio Larreina

    Pages 181-189

15. New Challenges and Roles of Metadata in Text/Data Mining in Statistics

    • Dušan Šoltés

    Pages 191-199

16. Using Text Mining in Official Statistics

    • Alf Fyhrlund, Bert Fridlund, Bo Sundgren

    Pages 201-211

17. Combining Text Mining and Information Retrieval Techniques for Enhanced Access to Statistical Data on the Web: A Preliminary Report

    • Martin Rajman, Martin Vesely

    Pages 213-222

18. Comparative Study of Text Mining Tools

    • Antoine Spinakis, Asanoula Chatzimakri

    Pages 223-232

19. Some Industrial Applications of Text Mining

    • Bernd Drewes

    Pages 233-238

20. Using Text Mining Tools for Event Data Analysis

    • Theoni Stathopoulou

    Pages 239-253

Text mining is an exciting application ?eld and an area of scienti?c - search that is currently under rapid development. It uses techniques from well-established scienti?c ?elds (e. g. data mining, machine learning, infor- tion retrieval, natural language processing, case-based reasoning, statistics and knowledge management) in an e?ort to help people gain insight, und- stand and interpret large quantities of (usually) semi-structured and unstr- tured data. Despite the advances made during the last few years, many issues remain unresolved. Proper co-ordination activities, dissemination of current trends and standardisation of the procedures have been identi?ed, as key needs. There are many questions still unanswered, especially to the potential users; what is the scope of Text Mining, who uses it and for what purpose, what constitutes the leading trends in the ?eld of Text Mining – especially in relation to IT – and whether there still remain areas to be covered. 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 scienti?c research, knowledge mining is principally concerned with the quantitative synthesis and visualization of - search results and ?ndings. The results of knowledge mining are increased scienti?c understanding along with improvements in research quality and value. Knowledge mining products can be used to highlight research opportunities, assist with the p- sentation of “best” scienti?c evidence, facilitate research portfolio mana- ment, as well as, facilitate policy setting and decision making.

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

• Research Academic Computer Technology, Patras, Greece

  Spiros Sirmakessis