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Semantics, Web and Mining

Joint International Workshop, EWMF 2005 and KDO 2005, Porto, Portugal, October 3-7, 2005, Revised Selected Papers

  • Conference proceedings
  • © 2006

Overview

Editors:
  1. Markus Ackermann

    1. Department of Natural Language Processing, Institute for Computer Science, University of Leipzig,  

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  2. Bettina Berendt

    1. Department of Computer Science, K.U. Leuven, Heverlee, Belgium

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  3. Marko Grobelnik

    1. Dept. of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia

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  4. Andreas Hotho

    1. Knowledge & Data Engineering Group, University of Kassel, Kassel, Germany

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  5. Dunja Mladenič

    1. Jožef Stefan Institute, Ljubljana, Slovenia

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  6. Giovanni Semeraro

    1. Dipartimento di Informatica, Università di Bari, Bari

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  7. Myra Spiliopoulou

    1. Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Germany

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  8. Gerd Stumme

    1. Research Center L3S, Hannover, Germany

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  9. Vojtěch Svátek

    1. Dept. Information and Knowledge Engineering, University of Economics, Prague, Prague, Czech Republic

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  10. Maarten Someren

    1. Human Computer Studies Lab, University of Amsterdam, Amsterdam, The Netherlands

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 4289)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: EWMF 2005, KDO 2005.

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Table of contents (12 papers)

  1. Front Matter

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  2. EWMF Papers

    1. A Website Mining Model Centered on User Queries

      • Ricardo Baeza-Yates, Barbara Poblete
      Pages 1-17

    2. WordNet-Based Word Sense Disambiguation for Learning User Profiles

      • M. Degemmis, P. Lops, G. Semeraro
      Pages 18-33

    3. Visibility Analysis on the Web Using Co-visibilities and Semantic Networks

      • Peter Kiefer, Klaus Stein, Christoph Schlieder
      Pages 34-50

    4. Link-Local Features for Hypertext Classification

      • Hervé Utard, Johannes Fürnkranz
      Pages 51-64

    5. Information Retrieval in Trust-Enhanced Document Networks

      • Klaus Stein, Claudia Hess
      Pages 65-81

    6. Semi-automatic Creation and Maintenance of Web Resources with webTopic

      • Nuno F. Escudeiro, Alípio M. Jorge
      Pages 82-102

  3. KDO Papers on KDD for Ontology

    1. Discovering a Term Taxonomy from Term Similarities Using Principal Component Analysis

      • Holger Bast, Georges Dupret, Debapriyo Majumdar, Benjamin Piwowarski
      Pages 103-120

    2. Semi-automatic Construction of Topic Ontologies

      • Blaž Fortuna, Dunja Mladenič, Marko Grobelnik
      Pages 121-131

    3. Evaluation of Ontology Enhancement Tools

      • Myra Spiliopoulou, Markus Schaal, Roland M. Müller, Marko Brunzel
      Pages 132-146

  4. KDO Papers on Ontology for KDD

    1. Introducing Semantics in Web Personalization: The Role of Ontologies

      • Magdalini Eirinaki, Dimitrios Mavroeidis, George Tsatsaronis, Michalis Vazirgiannis
      Pages 147-162

    2. Ontology-Enhanced Association Mining

      • Vojtěch Svátek, Jan Rauch, Martin Ralbovský
      Pages 163-179

    3. Ontology-Based Rummaging Mechanisms for the Interpretation of Web Usage Patterns

      • Mariângela Vanzin, Karin Becker
      Pages 180-195

  5. Back Matter

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Other volumes

  1. Semantics, Web and Mining

Keywords

About this book

Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralquestion is whether and to what extent the meaning extracted from the data is expressed in a formal way that allows not only humans but also machines to understand and re-use it, i. e. , whether the semantics are formal semantics. Conversely, the input to KD processes di?ers between KD projects and approaches. One central questioniswhetherthebackgroundknowledge,businessunderstanding,etc. that the analyst employs to improve the results of KD is a set of natural-language statements, a theory in a formal language, or somewhere in between. Also, the data that are being mined can be more or less structured and/or accompanied by formal semantics. These questions must be asked in every KD e?ort. Nowhere may they be more pertinent, however, than in KD from Web data (“Web mining”). Thisis due especially to the vast amounts and heterogeneity of data and ba- ground knowledge available for Web mining (content, link structure, and - age), and to the re-use of background knowledge and KD results over the Web as a global knowledge repository and activity space. In addition, the (Sem- tic) Web can serve as a publishing space for the results of knowledge discovery from other resources, especially if the whole process is underpinned by common ontologies.

Editors and Affiliations

  • Department of Natural Language Processing, Institute for Computer Science, University of Leipzig,  

    Markus Ackermann

  • Department of Computer Science, K.U. Leuven, Heverlee, Belgium

    Bettina Berendt

  • Dept. of Knowledge Technologies, Jozef Stefan Institute, Ljubljana, Slovenia

    Marko Grobelnik

  • Knowledge & Data Engineering Group, University of Kassel, Kassel, Germany

    Andreas Hotho

  • Jožef Stefan Institute, Ljubljana, Slovenia

    Dunja Mladenič

  • Dipartimento di Informatica, Università di Bari, Bari

    Giovanni Semeraro

  • Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Germany

    Myra Spiliopoulou

  • Research Center L3S, Hannover, Germany

    Gerd Stumme

  • Dept. Information and Knowledge Engineering, University of Economics, Prague, Prague, Czech Republic

    Vojtěch Svátek

  • Human Computer Studies Lab, University of Amsterdam, Amsterdam, The Netherlands

    Maarten Someren

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