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dc.contributor.authorChbeir, Richard*
dc.contributor.authorLuo, Yi*
dc.contributor.authorTekli, Joe*
dc.contributor.authorYetongnon, Kokou*
dc.contributor.authorRaymundo Ibañez, Carlos Arturo*
dc.contributor.authorTraina, Agma J. M.*
dc.contributor.authorTraina Jr, Caetano*
dc.contributor.authorAl Assad, Marc*
dc.creatorUniversidad Peruana de Ciencias Aplicadas (UPC)es_PE
dc.date.accessioned2015-02-10T05:46:42Z
dc.date.available2015-02-10T05:46:42Z
dc.date.issued2015-02-10
dc.identifier.citation[1] R. Chbeir, Y. Luo, J. Tekli, K. Yetongnon, and C. Raymundo, “SemIndex : Semantic-Aware Inverted Index,” Lect. Notes Comput. Sci., vol. 8716, pp. 290–307, 2014.eng
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-319-10933-6_22
dc.identifier.urihttp://hdl.handle.net/10757/344330
dc.description[email protected]es_PE
dc.description.abstractThis paper focuses on the important problem of semanticaware search in textual (structured, semi-structured, NoSQL) databases. This problem has emerged as a required extension of the standard containment keyword based query to meet user needs in textual databases and IR applications. We provide here a new approach, called SemIndex, that extends the standard inverted index by constructing a tight coupling inverted index graph that combines two main resources: a general purpose semantic network, and a standard inverted index on a collection of textual data. We also provide an extended query model and related processing algorithms with the help of SemIndex. To investigate its effectiveness, we set up experiments to test the performance of SemIndex. Preliminary results have demonstrated the effectiveness, scalability and optimality of our approach.
dc.formatapplication/pdfes_PE
dc.language.isoengeng
dc.publisherSpringer International Publishinges_PE
dc.relation.urlhttp://link.springer.com/chapter/10.1007/978-3-319-10933-6_22es_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.sourceUniversidad Peruana de Ciencias Aplicadas (UPC)es_PE
dc.sourceRepositorio Académico - UPCes_PE
dc.subjectSemantic Querieses_PE
dc.subjectInverted lndexes_PE
dc.subjectNoSQL indexinges_PE
dc.subjectSemantic Networkes_PE
dc.subjectOntologieses_PE
dc.titleSemIndex: Semantic-Aware Inverted Indexes_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.journalLecture Notes in Computer Scienceeng
dc.description.fundingThis study is partly funded by: Bourgogne Region program, CNRS, and STIC AmSud project Geo-Climate XMine, and LAU grant SOERC-1314T012.eng
dc.description.peer-reviewRevisión por pareses_PE
refterms.dateFOA2018-06-16T23:13:23Z
html.description.abstractThis paper focuses on the important problem of semanticaware search in textual (structured, semi-structured, NoSQL) databases. This problem has emerged as a required extension of the standard containment keyword based query to meet user needs in textual databases and IR applications. We provide here a new approach, called SemIndex, that extends the standard inverted index by constructing a tight coupling inverted index graph that combines two main resources: a general purpose semantic network, and a standard inverted index on a collection of textual data. We also provide an extended query model and related processing algorithms with the help of SemIndex. To investigate its effectiveness, we set up experiments to test the performance of SemIndex. Preliminary results have demonstrated the effectiveness, scalability and optimality of our approach.


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