BibSonomy publications for /user/eswc2008/activationWed May 28 14:50:00 CEST 2008Berlin, HeidelbergProceedings of the 5th European Semantic Web ConferenceJuneLNCSCombining Meta Data and Document Search with Spreading Activation for Semantic Desktop Search2008document semantic search spreading desktop activation retrieval The Semantic Desktop is a means to support users in Personal Information Management (PIM). It provides an excellent test bed for Semantic Web technology: resources (e. g., persons, projects, messages, documents) are distributed amongst multiple systems, ontologies are used to link and annotate them. Finding information is a core element in PIM. For the end user, the search interface has to be intuitive to use, natural language queries provide a simple mean to express requests. State of the art semantic search engines focus on metadata search or on semantic document retrieval. We combine both approaches to search the Semantic Desktop exploiting all available information, where we build on semantic teleporting and spreading activation. This combination is able to answer queries with instances, subgraphs of the knowledge base, and with relevant documents. We evaluated our approach on ESWC 2007 data in comparison with Google site search.Wed May 28 14:49:50 CEST 2008Berlin, HeidelbergProceedings of the 5th European Semantic Web ConferenceJuneLNCSSemantic Reasoning: A Path To New Possibilities of Personalization2008techniques activation associations web personalization semantic reasoning spreading user-interfaces-and-personalization Recommender systems face up to current information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest items similar to those the user liked in the past, by resorting to syntactic matching mechanisms. The rigid nature of such mechanisms leads to recommend only items that bear a strong resemblance to those the user already knows. In this paper, we propose a novel content-based strategy that diversifies the offered recommendations by employing reasoning mechanisms borrowed from the Semantic Web. These mechanisms discover extra knowledge about the user's preferences, thus favoring more accurate and flexible personalization processes. Our approach is generic enough to be used in a wide variety of personalization applications and services, in diverse domains and recommender systems. The proposed reasoning-based strategy has been empirically evaluated with a set of real users. The obtained results evidence computational feasibility and significant increases of recommendation accuracy in relation to existing approaches where our reasoning capabilities are disregarded.