Tailored semantic annotation for semantic search
Ver/ Abrir
Impacto
Scholar |
Otros documentos de la autoría: Berlanga Llavori, Rafael; Nebot Romero, Victoria; Pérez Catalán, María
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Tailored semantic annotation for semantic searchFecha de publicación
2015Editor
ElsevierISSN
1570-8268Cita bibliográfica
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. 69-81Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S1570826814000559Versión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
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. [-]
Publicado en
Web Semantics: Science, Services and Agents on the World Wide Web, 2015, vol. 30Derechos de acceso
Aparece en las colecciones
- LSI_Articles [362]