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

arXiv 2111.03000 ↗First posted 4 Nov 2021cs.CL cs.IR cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 16

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4 more
  1. 2021Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Basecited by 16
  2. 2017SPARQL as a Foreign Languagecited by 13
  3. 2018Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attentioncited by 8
  4. 2020Exploring Sequence-to-Sequence Models for SPARQL Pattern Compositioncited by 1

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