Han, LushanFinin, Tim2018-11-262018-11-262008-07-13Lushan 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.pdfhttp://hdl.handle.net/11603/12083Proceedings of the Twenty-Third AAAI Conference on Artificial IntelligenceThe 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.2 pagesen-USThis 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.Semantic WebWordsnamespace statisticsWordNet lexical ontologyUMBC Ebiquity Research GroupText