Tailored semantic annotation for semantic search
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Other documents of the author: Berlanga Llavori, Rafael; Nebot Romero, Victoria; Pérez Catalán, María
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Title
Tailored semantic annotation for semantic searchDate
2015Publisher
ElsevierISSN
1570-8268Bibliographic citation
BERLANGA, Rafael; NEBOT, Victoria; PÉREZ, Maria. Tailored semantic annotation for semantic search. Web Semantics: Science, Services and Agents on the World Wide Web, 2015, vol. 30, p. 69-81Type
info:eu-repo/semantics/articlePublisher version
http://www.sciencedirect.com/science/article/pii/S1570826814000559Version
info:eu-repo/semantics/submittedVersionSubject
Abstract
This paper presents a novel method for semantic annotation and search of a target corpus using several knowledge resources (KRs). This method relies on a formal statistical framework in which KR concepts and corpus ... [+]
This paper presents a novel method for semantic annotation and search of a target corpus using several knowledge resources (KRs). This method relies on a formal statistical framework in which KR concepts and corpus documents are homogeneously represented using statistical language models. Under this framework, we can perform all the necessary operations for an efficient and effective semantic annotation of the corpus. Firstly, we propose a coarse tailoring of the KRs w.r.t the target corpus with the main goal of reducing the ambiguity of the annotations and their computational overhead. Then, we propose the generation of concept profiles, which allow measuring the semantic overlap of the KRs as well as performing a finer tailoring of them. Finally, we propose how to semantically represent documents and queries in terms of the KRs concepts and the statistical framework to perform semantic search. Experiments have been carried out with a corpus about web resources which includes several Life Sciences catalogs and Wikipedia pages related to web resources in general (e.g., databases, tools, services, etc.). Results demonstrate that the proposed method is more effective and efficient than state-of-the-art methods relying on either context-free annotation or keyword-based search. [-]
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Web Semantics: Science, Services and Agents on the World Wide Web, 2015, vol. 30Rights
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