Reducing the impact of out of vocabulary words in the translation of natural language questions into SPARQL queries
This addresses the issue of accessing knowledge bases for users unfamiliar with SPARQL, though it is incremental as it builds on existing neural-machine translation systems.
The paper tackled the problem of out-of-vocabulary (OOV) words in translating natural language questions to SPARQL queries, and the result was a combined approach using Named Entity Linking, Named Entity Recognition, and Neural Machine Translation that demonstrated improved effectiveness and resilience compared to existing methods on datasets like Monument, QALD-9, and LC-QuAD v1.
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.