  Accessing the large volumes of information available in public knowledge
bases might be complicated for those users unfamiliar with the SPARQL query
language. Automatic translation of questions posed in natural language in
SPARQL has the potential of overcoming this problem. Existing systems based on
neural-machine translation are very effective but easily fail in recognizing
words that are Out Of the Vocabulary (OOV) of the training set. This is a
serious issue while querying large ontologies. In this paper, we combine Named
Entity Linking, Named Entity Recognition, and Neural Machine Translation to
perform automatic translation of natural language questions into SPARQL
queries. We demonstrate empirically that our approach is more effective and
resilient to OOV words than existing approaches by running the experiments on
Monument, QALD-9, and LC-QuAD v1, which are well-known datasets for Question
Answering over DBpedia.
