Conference paper
Feature-based Ontology Mapping from an Information Receivers’ Viewpoint
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.
Language: | English |
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Publisher: | SciTePress |
Year: | 2012 |
Pages: | 34-43 |
Proceedings: | 9th International Workshop on Natural Language Processing and Cognitive Science (NLPCS 2012)Natural Language Processing and Cognitive Science |
ISBN: | 9898565160 and 9789898565167 |
Types: | Conference paper |
ORCIDs: | Mørup, Morten |