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Content

Inhalt: OIL and DAML + OIL: Ontology Languages for the Semantic Web (pages 11-31) / Dieter Fensel, Frank van Harmelen and Ian Horrocks A Methodology for Ontology-Based Knowledge Management (pages 33-46) / York Sure and Rudi Studer Ontology Management: Storing, Aligning and Maintaining Ontologies (pages 47-69) / Michel Klein, Ying Ding, Dieter Fensel and Borys Omelayenko Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema (pages 71-89) / Jeen Broekstra, Arjohn Kampman and Frank van Harmelen Generating Ontologies for the Semantic Web: OntoBuilder (pages 91-115) / R. H. P. Engels and T. Ch. Lech OntoEdit: Collaborative Engineering of Ontologies (pages 117-132) / York Sure, Michael Erdmann and Rudi Studer QuizRDF: Search Technology for the Semantic Web (pages 133-144) / John Davies, Richard Weeks and Uwe Krohn Spectacle (pages 145-159) / Christiaan Fluit, Herko ter Horst, Jos van der Meer, Marta Sabou and Peter Mika OntoShare: Evolving Ontologies in a Knowledge Sharing System (pages 161-177) / John Davies, Alistair Duke and Audrius Stonkus Ontology Middleware and Reasoning (pages 179-196) / Atanas Kiryakov, Kiril Simov and Damyan Ognyanov Ontology-Based Knowledge Management at Work: The Swiss Life Case Studies (pages 197-218) / Ulrich Reimer, Peter Brockhausen, Thorsten Lau and Jacqueline R. Reich Field Experimenting with Semantic Web Tools in a Virtual Organization (pages 219-244) / Victor Iosif, Peter Mika, Rikard Larsson and Hans Akkermans A Future Perspective: Exploiting Peer-To-Peer and the Semantic Web for Knowledge Management (pages 245-264) / Dieter Fensel, Steffen Staab, Rudi Studer, Frank van Harmelen and John Davies Conclusions: Ontology-driven Knowledge Management - Towards the Semantic Web? (pages 265-266) / John Davies, Dieter Fensel and Frank van Harmelen

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Date

23. 3.2008 19:10:22

Footnote

Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.