Abstract
In the context of ontology alignment, linguistic analysis is a prominent solution, used by various proposed methodologies. When mapping ontologies that use the same language, the existent approaches have been shown to produce significant results, being able to handle complex descriptions of the enclosed concepts and properties. In order to expand the applied linguistic methods in a cross-language context, i.e. to align ontologies that use different languages, it is essential to automate the process of finding lexical correspondences, beyond simple term translation, between the entity descriptions provided by the involved ontologies. The present paper proposes a machine learning approach to obtain the optimal from a set of translation provided by different automated machine translation services, in order to use it as the basis for aligning ontology pairs that provide complex descriptions expressed in different languages.
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Koukourikos, A., Karampiperis, P., Stoitsis, G. (2013). Cross-Language Ontology Alignment Utilizing Machine Translation Models. In: Garoufallou, E., Greenberg, J. (eds) Metadata and Semantics Research. MTSR 2013. Communications in Computer and Information Science, vol 390. Springer, Cham. https://doi.org/10.1007/978-3-319-03437-9_9
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DOI: https://doi.org/10.1007/978-3-319-03437-9_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03436-2
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