Ricardo Baeza-Yates, Barbara Poblete (auth.), Markus Ackermann, Bettina Berendt, Marko Grobelnik, Andreas Hotho, Dunja Mladenič, Giovanni Semeraro, Myra Spiliopoulou, Gerd Stumme, Vojtěch Svátek, Maarten van Someren (eds.)3540476970
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. |
Table of contents :
Front Matter….Pages –
A Website Mining Model Centered on User Queries….Pages 1-17
WordNet-Based Word Sense Disambiguation for Learning User Profiles….Pages 18-33
Visibility Analysis on the Web Using Co-visibilities and Semantic Networks….Pages 34-50
Link-Local Features for Hypertext Classification….Pages 51-64
Information Retrieval in Trust-Enhanced Document Networks….Pages 65-81
Semi-automatic Creation and Maintenance of Web Resources with webTopic….Pages 82-102
Discovering a Term Taxonomy from Term Similarities Using Principal Component Analysis….Pages 103-120
Semi-automatic Construction of Topic Ontologies….Pages 121-131
Evaluation of Ontology Enhancement Tools….Pages 132-146
Introducing Semantics in Web Personalization: The Role of Ontologies….Pages 147-162
Ontology-Enhanced Association Mining….Pages 163-179
Ontology-Based Rummaging Mechanisms for the Interpretation of Web Usage Patterns….Pages 180-195
Back Matter….Pages – |
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