Feature-based Ontology Mapping from an Information Receivers’ Viewpoint

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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This paper compares four algorithms for computing feature-based similarities between concepts respectively possessing a distinctive set of features. The eventual purpose of comparing these feature-based similarity algorithms is to identify a candidate term in a Target Language (TL) that can optimally convey the original meaning of a culturally-specific Source Language (SL) concept to a TL audience by aligning two culturally-dependent domain-specific ontologies. The results indicate that the Bayesian Model of Generalization [1] performs best, not only for identifying candidate translation terms, but also for computing probabilities that an information receiver successfully infers the meaning of an SL concept from a given TL translation.
Original languageEnglish
Title of host publicationProceedings of the 9th International Workshop on Natural Language Processing and Cognitive Science, NLPCS 2012
PublisherSciTePress
Publication date2012
Pages34-43
ISBN (print)9789898565167
StatePublished

Workshop

Workshop9th International Workshop on Natural Language Processing and Cognitive Science (NLPCS 2012)
CountryPoland
CityWroclaw
Period28/06/12 → …
Internet addresshttp://www.iceis.org/?y=2012

Keywords

  • Algorithms, Bayesian networks, Computational linguistics, Translation (languages), Natural language processing systems
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