By Yeliz Yesilada, Sean Bechhofer (auth.), Dr. Manolis Wallace, Professor Marios C. Angelides, Dr. Phivos Mylonas (eds.)

Realizing the starting to be significance of semantic model and personalization of media, the editors of this booklet introduced jointly major researchers and practitioners of the sphere to debate the cutting-edge, and discover rising fascinating advancements. This quantity includes prolonged types of chosen papers awarded on the 1st foreign Workshop on Semantic Media model and Personalization (SMAP 2006), which came about in Athens in December 2006.

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The requested document is processed by looking for occurrences of these terms. Any that are found provide potential link sources (see Fig. 1b). 3. For the identified link sources, the DLS agent queries the RM to determine appropriate targets; it also uses the knowledge about the term encoded in the knowledge resource to enhance the suggested link targets – using narrower, broader and related terms to suggest more resources. For example, in Fig. 1 a list of resources are suggested for the term “Ultrasparc iv” along with a list of resources for related terms such as “Sun Fire E4900 Server”, etc.

The proxy rewrites documents, inserting links where appropriate. This approach also potentially opens up the use of COHSE to alternative platforms such as WAP and mobile phones as appropriate transformations an be used for different clients. As with server side deployment, no end user installation of specialist browsing software is required. However, the use of a proxy may introduce processing delays and the need to re-write URLs to ensure that users continue to browse via the proxy. Moreover users may need to modify their browser’s configuration which can be inconvenient and might not be allowed in some Intranets.

Popular online services such as Google [1, 37] or Amazon [26, 32] are nowadays exploiting some personalisation features, in particular to improve their content retrieval systems. Even if these systems have the merit of having been deployed at a large scale, they rely on rather simple models, which may often be inaccurate or still provide results that do not completely match I. com 26 I. Cantador et al. users’ expectations. Indeed, personalising a content retrieval system involves considerable complexity, mainly because finding implicit evidence of user needs and interests through their behaviour is not an easy task.

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