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dc.contributor.authorNebot Romero, María Victoria
dc.contributor.authorBerlanga Llavori, Rafael
dc.date.accessioned2015-05-29T09:34:32Z
dc.date.available2015-05-29T09:34:32Z
dc.date.issued2014
dc.identifier.citationNEBOT, Victoria; BERLANGA, Rafael. Exploiting Semantic Annotations for Open Information Extraction: an experience in the biomedical domain. Knowledge and information Systems, 2014, 38.2: 365-389.ca_CA
dc.identifier.issn0219-1377
dc.identifier.issn0219-3116
dc.identifier.urihttp://hdl.handle.net/10234/122166
dc.description.abstractThe increasing amount of unstructured text published on the Web is demanding new tools and methods to automatically process and extract relevant information. Traditional information extraction has focused on harvesting domain-specific, pre-specified relations, which usually requires manual labor and heavy machinery; especially in the biomedical domain, the main efforts have been directed toward the recognition of well-defined entities such as genes or proteins, which constitutes the basis for extracting the relationships between the recognized entities. The intrinsic features and scale of the Web demand new approaches able to cope with the diversity of documents, where the number of relations is unbounded and not known in advance. This paper presents a scalable method for the extraction of domain-independent relations from text that exploits the knowledge in the semantic annotations. The method is not geared to any specific domain (e.g., protein–protein interactions and drug–drug interactions) and does not require any manual input or deep processing. Moreover, the method uses the extracted relations to compute groups of abstract semantic relations characterized by their signature types and synonymous relation strings. This constitutes a valuable source of knowledge when constructing formal knowledge bases, as we enable seamless integration of the extracted relations with the available knowledge resources through the process of semantic annotation. The proposed approach has successfully been applied to a large text collection in the biomedical domain and the results are very encouraging.ca_CA
dc.description.sponsorShipThe work was supported by the CICYT project TIN2011-24147 from the Spanish Ministry of Economy and Competitiveness (MINECO).ca_CA
dc.format.extent24 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringerca_CA
dc.relation.isPartOfKnowledge and information Systems, 2014, 38.2ca_CA
dc.rights© Springer International Publishing AGca_CA
dc.subjectUnsupervised IEca_CA
dc.subjectRelation extractionca_CA
dc.subjectSemantic annotationca_CA
dc.subjectBiomedical domainca_CA
dc.titleExploiting semantic annotations for open information extraction: an experience in the biomedical domainca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp://dx.doi.org/10.1007/s10115-012-0590-x
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttp://link.springer.com/article/10.1007%2Fs10115-012-0590-xca_CA


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  • LSI_Articles [268]
    Articles de publicacions periòdiques escrits per professors del Departament de Llenguatges i Sistemes Informàtics

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