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Learning Embeddings to lexicalise RDF Properties

Abstract : A difficult task when generating text from knowledge bases (KB) consists in finding appropriate lexicalisations for KB symbols. We present an approach for lexicalis-ing knowledge base relations and apply it to DBPedia data. Our model learns low-dimensional embeddings of words and RDF resources and uses these representations to score RDF properties against candidate lexicalisations. Training our model using (i) pairs of RDF triples and automatically generated verbalisations of these triples and (ii) pairs of paraphrases extracted from various resources, yields competitive results on DBPedia data.
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Submitted on : Wednesday, October 25, 2017 - 4:49:08 PM
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Laura Perez-Beltrachini, Claire Gardent. Learning Embeddings to lexicalise RDF Properties. *SEM 2016,. The Fifth Joint Conference on Lexical and Computational Semantics., Aug 2016, Berlin, Germany. pp.219 - 228, ⟨10.18653/v1/S16-2027⟩. ⟨hal-01623812⟩



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