Semantics, Web and Mining

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

  • Markus Ackermann
  • Bettina Berendt
  • Marko Grobelnik
  • Andreas Hotho
  • Dunja Mladenič
  • Giovanni Semeraro
  • Myra Spiliopoulou
  • Gerd Stumme
  • Vojtěch Svátek
  • Maarten van Someren
Conference proceedings EWMF 2005, KDO 2005

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

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 4289)

Table of contents

  1. Front Matter
  2. EWMF Papers

    1. Ricardo Baeza-Yates, Barbara Poblete
      Pages 1-17
    2. M. Degemmis, P. Lops, G. Semeraro
      Pages 18-33
    3. Peter Kiefer, Klaus Stein, Christoph Schlieder
      Pages 34-50
    4. Hervé Utard, Johannes Fürnkranz
      Pages 51-64
    5. Klaus Stein, Claudia Hess
      Pages 65-81
    6. Nuno F. Escudeiro, Alípio M. Jorge
      Pages 82-102
  3. KDO Papers on KDD for Ontology

    1. Holger Bast, Georges Dupret, Debapriyo Majumdar, Benjamin Piwowarski
      Pages 103-120
    2. Blaž Fortuna, Dunja Mladenič, Marko Grobelnik
      Pages 121-131
    3. Myra Spiliopoulou, Markus Schaal, Roland M. Müller, Marko Brunzel
      Pages 132-146
  4. KDO Papers on Ontology for KDD

    1. Magdalini Eirinaki, Dimitrios Mavroeidis, George Tsatsaronis, Michalis Vazirgiannis
      Pages 147-162
    2. Vojtěch Svátek, Jan Rauch, Martin Ralbovský
      Pages 163-179
  5. Back Matter

About these proceedings


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”). This is 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.


association rule mining classification data mining hyperlink mining knowledge discovery navigation analysis ontologies pattern mining recommender systems semantic Web semantic Web mining taxonomic context usage analysis usage pattern discover

Editors and affiliations

  • Markus Ackermann
    • 1
  • Bettina Berendt
    • 2
  • Marko Grobelnik
    • 3
  • Andreas Hotho
    • 4
  • Dunja Mladenič
    • 5
  • Giovanni Semeraro
    • 6
  • Myra Spiliopoulou
    • 7
  • Gerd Stumme
    • 8
  • Vojtěch Svátek
    • 9
  • Maarten van Someren
    • 10
  1. 1.Department of Natural Language Processing, Institute for Computer ScienceUniversity of Leipzig 
  2. 2.Department of Computer ScienceK.U. LeuvenHeverleeBelgium
  3. 3.Dept. of Knowledge TechnologiesJozef Stefan InstituteLjubljanaSlovenia
  4. 4.Knowledge & Data Engineering GroupUniversity of KasselKasselGermany
  5. 5.Jožef Stefan InstituteLjubljanaSlovenia
  6. 6.Dipartimento di InformaticaUniversità di BariBari
  7. 7.Faculty of Computer ScienceOtto-von-Guericke-University MagdeburgGermany
  8. 8.Research Center L3SHannoverGermany
  9. 9.Dept. Information and Knowledge EngineeringUniversity of Economics, PraguePragueCzech Republic
  10. 10.Human Computer Studies LabUniversity of AmsterdamAmsterdamThe Netherlands

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