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

arXiv: https://arxiv.org/abs/2111.03000 · HTML: https://mlsanity.com/paper/2111.03000/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System](https://mlsanity.com/paper/1802.08979/) (2018)
- [Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions](https://mlsanity.com/paper/1909.00786/) (2019)
- [Neural Machine Translating from Natural Language to SPARQL](https://mlsanity.com/paper/1906.09302/) (2019)
- [Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base](https://mlsanity.com/paper/2109.03614/) (2021)
- [SPARQL as a Foreign Language](https://mlsanity.com/paper/1708.07624/) (2017)
- [Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention](https://mlsanity.com/paper/1803.04329/) (2018)
- [Exploring Sequence-to-Sequence Models for SPARQL Pattern Composition](https://mlsanity.com/paper/2010.10900/) (2020)

## 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.