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Linking knowledge graphs across languages with semantic similarity and machine translation

McCrae, John P.
; 
Arcan, Mihael
; 
Buitelaar, Paul
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Identifiers
http://hdl.handle.net/10379/14882
https://doi.org/10.13025/21197
Repository DOI
Publication Date
2017-09-04
Keywords
Linking, Knowledge graphs, Languages, Semantic similarity, Machine translation
Type
Conference Paper
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Citation
McCrae, John P., Arcan, Mihael, & Buitelaar, Paul. (2017). Linking knowledge graphs across languages with semantic similarity and machine translation. Paper presented at the MLP 2017 The First Workshop on Multi-Language Processing in a Globalising World, Dublin City University, Dublin, 04-05 September.
Abstract
Knowledge graphs and ontologies underpin many natural language processing applications, and to apply these to new languages, these knowledge graphs must be translated. Up until now, this has been achieved either by direct label translation or by cross-lingual alignment, which matches the concepts in the graph to another graph in the target languages. We show that these two approaches can, in fact, be combined and that the combination of machine translation and crosslingual alignment can obtain improved results for translating a biomedical ontology from English to German.
Funder
Science Foundation Ireland
Publisher
MLP 2017
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
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Collections
Data Science Institute (Conference Papers)