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Information extraction for knowledge base construction in the music domain

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dc.contributor.author Oramas, Sergio
dc.contributor.author Espinosa-Anke, Luis
dc.contributor.author Sordo, Mohamed
dc.contributor.author Saggion, Horacio
dc.contributor.author Serra, Xavier
dc.date.accessioned 2017-11-28T10:19:42Z
dc.date.available 2017-11-28T10:19:42Z
dc.date.issued 2016
dc.identifier.citation Oramas S, Espinosa-Anke L, Sordo M, Saggion H, Serra X. Information extraction for knowledge base construction in the music domain. Data Knowl Eng. 2016;106:70-83. DOI: 10.1016/j.datak.2016.06.001
dc.identifier.issn 0169-023X
dc.identifier.uri http://hdl.handle.net/10230/33366
dc.description.abstract The rate at which information about music is being created and shared on the web is growing exponentially. However, the challenge of making sense of all this data remains an open problem. In this paper, we present and evaluate an Information Extraction pipeline aimed at the construction of a Music Knowledge Base. Our approach starts off by collecting thousands of stories about songs from the songfacts.com website. Then, we combine a state-of-the-art Entity Linking tool and a linguistically motivated rule-based algorithm to extract semantic relations between entity pairs. Next, relations with similar semantics are grouped into clusters by exploiting syntactic dependencies. These relations are ranked thanks to a novel confidence measure based on statistical and linguistic evidence. Evaluation is carried out intrinsically, by assessing each component of the pipeline, as well as in an extrinsic task, in which we evaluate the contribution of natural language explanations in music recommendation. We demonstrate that our method is able to discover novel facts with high precision, which are missing in current generic as well as music-specific knowledge repositories.
dc.description.sponsorship This work is partially funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Programme (MDM-2015-0502), and under the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof Data & knowledge engineering. 2016;106:70-83.
dc.relation.isreferencedby http://hdl.handle.net/10230/27021
dc.rights © Elsevier http://dx.doi.org/10.1016/j.datak.2016.06.001
dc.title Information extraction for knowledge base construction in the music domain
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1016/j.datak.2016.06.001
dc.subject.keyword Relation extraction
dc.subject.keyword Entity linking
dc.subject.keyword Knowledge base construction
dc.subject.keyword Music recommendation
dc.subject.keyword Semantic web
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-65308-C5-5-R
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/submittedVersion


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