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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Artikelnr. des Verlages: 11908678, 978-3-540-47697-9
2006
Seitenzahl: 216
Erscheinungstermin: 29. November 2006
Englisch
Abmessung: 235mm x 155mm x 12mm
Gewicht: 334g
ISBN-13: 9783540476979
ISBN-10: 3540476970
Artikelnr.: 20947151
Autorenporträt
Markus Ackermann, University of Leipzig, Germany / Bettina Berendt, Humboldt-Universität zu Berlin, Germany / Marko Grobelnik, J. Stefan Institute, Ljubliana, Slovenia / Andreas Hotho, University of Kassel, Germany / Dunja Mladenic, J. Stefan Institute, Ljubliana, Slovenia / Giovanni Semeraro, University of Bari, Italy / Myra Spiliopoulou, University of Magdeburg, Germany / Gerd Stumme, Universität Kassel, Germany / Vojtech Svatek, University of Economics, Prague, Czech Republic / Maarten van Someren, University of Amsterdam, Netherlands
Inhaltsangabe
EWMF Papers.- A Website Mining Model Centered on User Queries.- WordNet-Based Word Sense Disambiguation for Learning User Profiles.- Visibility Analysis on the Web Using Co-visibilities and Semantic Networks.- Link-Local Features for Hypertext Classification.- Information Retrieval in Trust-Enhanced Document Networks.- Semi-automatic Creation and Maintenance of Web Resources with webTopic.- KDO Papers on KDD for Ontology.- Discovering a Term Taxonomy from Term Similarities Using Principal Component Analysis.- Semi-automatic Construction of Topic Ontologies.- Evaluation of Ontology Enhancement Tools.- KDO Papers on Ontology for KDD.- Introducing Semantics in Web Personalization: The Role of Ontologies.- Ontology-Enhanced Association Mining.- Ontology-Based Rummaging Mechanisms for the Interpretation of Web Usage Patterns.
EWMF Papers.- A Website Mining Model Centered on User Queries.- WordNet-Based Word Sense Disambiguation for Learning User Profiles.- Visibility Analysis on the Web Using Co-visibilities and Semantic Networks.- Link-Local Features for Hypertext Classification.- Information Retrieval in Trust-Enhanced Document Networks.- Semi-automatic Creation and Maintenance of Web Resources with webTopic.- KDO Papers on KDD for Ontology.- Discovering a Term Taxonomy from Term Similarities Using Principal Component Analysis.- Semi-automatic Construction of Topic Ontologies.- Evaluation of Ontology Enhancement Tools.- KDO Papers on Ontology for KDD.- Introducing Semantics in Web Personalization: The Role of Ontologies.- Ontology-Enhanced Association Mining.- Ontology-Based Rummaging Mechanisms for the Interpretation of Web Usage Patterns.
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