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- 108 creator daniela-petrelli.
- 108 creator fabio-ciravegna.
- 108 creator ravish-bhagdev.
- 108 creator sam-chapman.
- 108 creator vitaveska-lanfranchi.
- 108 type InProceedings.
- 108 label "Hybrid Search: Effectively Combining Keywords and Ontology-based Searches".
- 108 sameAs 108.
- 108 abstract "This paper describes hybrid search, a semantic methodology supporting both document and knowledge retrieval via the flexible combination of ontology-based search and keyword-matching. Hybrid search aims to smoothly cope with lack of semantic coverage of document content, which is one of the main limitations of current semantic search methods. In this paper we define hybrid search formally, discuss its compatibility with the current semantic trends and present a reference implementation: K-Search. We then show how the methodology outperforms both keyword-based search and pure semantic search in terms of precision and recall in a set of experiments performed on a very large collection of documents. Experiments carried out with professional users show that users understand the paradigm and consider it very powerful and reliable. K-Search has been ported to 2 applications released at Rolls-Royce plc for searching technical documentation about jet engines.".
- 108 hasAuthorList authorList.
- 108 hasTopic Electronic_business.
- 108 hasTopic Search_engine.
- 108 hasTopic Semantic_Web.
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- 108 keyword "semantic search".
- 108 keyword "semantic web in use".
- 108 title "Hybrid Search: Effectively Combining Keywords and Ontology-based Searches".
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- 11 creator alberto-gil-solla.
- 11 creator jose-j-pazos-arias.
- 11 creator manuel-ramos-cabrer.
- 11 creator martin-lopez-nores.
- 11 creator yolanda-blanco-fernandez.
- 11 type InProceedings.
- 11 label "Semantic Reasoning: A Path To New Possibilities of Personalization".
- 11 sameAs 11.
- 11 abstract "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.".
- 11 hasAuthorList authorList.
- 11 hasTopic Personalization.
- 11 hasTopic Search_engine.
- 11 hasTopic Semantic_Web.
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- 11 keyword "personalization".
- 11 keyword "semantic associations".
- 11 keyword "semantic reasoning".
- 11 keyword "semantic web".
- 11 keyword "spreading activation techniques".
- 11 title "Semantic Reasoning: A Path To New Possibilities of Personalization".
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- 115 creator evren-sirin.
- 115 creator naiwen-lin.