Predicting Appropriate Semantic Web Terms from Words

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dc.contributor.authorHan, Lushan
dc.contributor.authorFinin, Tim
dc.date.accessioned2018-11-26T15:49:36Z
dc.date.available2018-11-26T15:49:36Z
dc.date.issued2008-07-13
dc.descriptionProceedings of the Twenty-Third AAAI Conference on Artificial Intelligenceen_US
dc.description.abstractThe Semantic Web language RDF was designed to unambiguously define and use ontologies to encode data and knowledge on the Web. Many people find it difficult, however, to write complex RDF statements and queries because doing so requires familiarity with the appropriate ontologies and the terms they define. We describe a system that suggests appropriate RDF terms given semantically related English words and general domain and context information. We use the Swoogle Semantic Web search engine to provide RDF term and namespace statistics, the WordNet lexical ontology to find semantically related words, and a naive Bayes classifier to suggest terms. A customized graph data structure of related namespaces is constructed from Swoogle's database to speed up the classifier model learning and prediction time.en_US
dc.description.urihttps://www.aaai.org/Papers/AAAI/2008/AAAI08-291.pdfen_US
dc.format.extent2 pagesen_US
dc.genreconference papers and proceedings preprintsen_US
dc.identifierdoi:10.13016/M28C9R77Z
dc.identifier.citationLushan Han and Tim Finin, Predicting Appropriate Semantic Web Terms from Words, Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 2008, https://www.aaai.org/Papers/AAAI/2008/AAAI08-291.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/12083
dc.language.isoen_USen_US
dc.publisherAAAIen_US
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.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectSemantic Weben_US
dc.subjectWordsen_US
dc.subjectnamespace statisticsen_US
dc.subjectWordNet lexical ontologyen_US
dc.subjectUMBC Ebiquity Research Groupen_US
dc.titlePredicting Appropriate Semantic Web Terms from Wordsen_US
dc.typeTexten_US

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