A Probabilistic Framework for Semantic Similarity and Ontology Mapping

dc.contributor.authorPeng, Yun
dc.contributor.authorDing, Zhongli
dc.contributor.authorPan, Rong
dc.contributor.authorYu, Yang
dc.contributor.authorKulvatunyou, Boonserm
dc.contributor.authorIvezik, Nenad
dc.contributor.authorJones, Albert
dc.contributor.authorCho, Hyunbo
dc.date.accessioned2018-11-29T18:29:55Z
dc.date.available2018-11-29T18:29:55Z
dc.date.issued2007-05-19
dc.descriptionProceedings of the 2007 Industrial Engineering Research Conferenceen
dc.description.abstractWe propose a probabilistic framework to address uncertainty in ontology-based semantic integration and interopera- tion. This framework consists of three main components: 1) BayesOWL that translates an OWL ontology to a Bayesian network, 2) SLBN (Semantically Linked Bayesian Networks) that support reasoning across translated BNs, and 3) a Learner that learns from the web the probabilities needed by the other modules. This framework expands the semantic web and can serve as a theoretical basis for solving real world semantic integration problems.en
dc.description.sponsorshipThis work was supported in part by NSF award IIS-0326460 and NIST award 60NANB6D6206.en
dc.description.urihttps://ebiquity.umbc.edu/paper/html/id/389/A-Probabilistic-Framework-for-Semantic-Similarity-and-Ontology-Mappingen
dc.format.extent6 pagesen
dc.genreconference papers and proceedings preprintsen
dc.identifierdoi:10.13016/M2GH9BD60
dc.identifier.urihttp://hdl.handle.net/11603/12129
dc.language.isoenen
dc.publisherInstitute of Industrial Engineersen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsPublic Domain Mark 1.0*
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.subjectSemantic weben
dc.subjectuncertaintyen
dc.subjectintegrationen
dc.subjectontologyen
dc.subjectBayesian networksen
dc.subjectUMBC Ebiquity Research Groupen
dc.titleA Probabilistic Framework for Semantic Similarity and Ontology Mappingen
dc.typeTexten

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