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Accessing the large volumes of information available in public knowledgebases might be complicated for those users unfamiliar with the SPARQL querylanguage. Automatic translation of questions posed in natural language inSPARQL has the potential of overcoming this problem. Existing systems based onneural-machine translation are very effective but easily fail in recognizingwords that are Out Of the Vocabulary (OOV) of the training set. This is aserious issue while querying large ontologies. In this paper, we combine NamedEntity Linking, Named Entity Recognition, and Neural Machine Translation toperform automatic translation of natural language questions into SPARQLqueries. We demonstrate empirically that our approach is more effective andresilient to OOV words than existing approaches by running the experiments onMonument, QALD-9, and LC-QuAD v1, which are well-known datasets for QuestionAnswering over DBpedia.

Reducing the impact of out of vocabulary words in the translation of natural language questions into SPARQL queries