Download Adaptive and Personalized Semantic Web by Christos Makris, Yannis Panagis (auth.), Spiros Sirmakessis PDF

By Christos Makris, Yannis Panagis (auth.), Spiros Sirmakessis Dr. (eds.)

ISBN-10: 3540306056

ISBN-13: 9783540306054

ISBN-10: 3540332790

ISBN-13: 9783540332794

Web Personalization might be outlined as any set of activities that may tailor the net adventure to a selected consumer or set of clients. to accomplish powerful personalization, enterprises needs to depend upon all on hand facts, together with the utilization and click-stream information (reflecting consumer behaviour), the location content material, the location constitution, area wisdom, in addition to person demographics and profiles. moreover, effective and clever thoughts are had to mine this information for actionable wisdom, and to successfully use the stumbled on wisdom to augment the clients' net adventure. the purpose of the overseas Workshop on Adaptive and custom-made Semantic internet that used to be held within the 16th ACM convention on Hypertext and Hypermedia (September 6-9, 2005, Salzburg, Austria) was once to compile researchers and practitioners within the fields of internet engineering, adaptive hypermedia, semantic net applied sciences, wisdom administration, info retrieval, person modelling, and different comparable disciplines which offer allowing applied sciences for customization and model at the world-wide-web. The booklet comprises the papers awarded throughout the workshop. shows of the papers can be found on-line at

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Extra resources for Adaptive and Personalized Semantic Web

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We will attempt to capture model smells that is, certain blocks in the Web application’s model that imply the possibility of refactoring. Both notions were initially introduced in a previous work [44] addressing the specific domain of personalization in Web applications. The approach we follow in the current paper is much broader and refers to the generalized process of conceptual modeling. More specifically, in this paper we provide a methodology in order to evaluate the conceptual schema of an application, by means of the design features incorporated in the application model.

Finally, we try to locate compositions of identical hypertext elements referring to exactly the same content interconnected with different link topologies. Ignoring the edges in the first set of graphs, except from those representing containment, we mine identical hypertext configurations within a graph or among graphs. Then, we filter the sets of subgraphs acquired utilizing the information represented in the second set of graphs (source entities), and keep those compositions that refer to the exact same data.

Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3):67–72, 1997. 6. D. Barbara. Requirements for clustering data streams. ACM SIGKDD Explorations Newsletter, 3(2):23–27, 2002. 7. J. Borges and M. Levene. Data mining of user navigation patterns. A. A. S. Newton, editors, Web Usage Analysis and User Profiling, Lecture Notes in Computer Science, pp. 92–111. Springer-Verlag, 1999. A Multi-Layered and Multi-Faceted Framework 33 8. P. Bradley, U. Fayyad, and C. Reina. Scaling clustering algorithms to large databases.

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