# Neural Machine Translating from Natural Language to SPARQL

arXiv: https://arxiv.org/abs/1906.09302 · HTML: https://mlsanity.com/paper/1906.09302/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 23 of 23)

- [Knowledge Base Question Answering: A Semantic Parsing Perspective](https://mlsanity.com/paper/2209.04994/) (2022)
- [Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis](https://mlsanity.com/paper/2201.08174/) (2022)
- [Fine-tuning Large Enterprise Language Models via Ontological Reasoning](https://mlsanity.com/paper/2306.10723/) (2023)
- [A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions](https://mlsanity.com/paper/2304.07772/) (2023)
- [Knowledge Graph Question Answering using Graph-Pattern Isomorphism](https://mlsanity.com/paper/2103.06752/) (2021)
- [An Empirical Study on Leveraging Position Embeddings for Target-oriented Opinion Words Extraction](https://mlsanity.com/paper/2109.01238/) (2021)
- [Financial data analysis application via multi-strategy text processing](https://mlsanity.com/paper/2204.11394/) (2022)
- [SPARKLE: Enhancing SPARQL Generation with Direct KG Integration in Decoding](https://mlsanity.com/paper/2407.01626/) (2024)
- [A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine Translation](https://mlsanity.com/paper/2211.10271/) (2022)
- [Question Answering over Knowledge Graphs with Neural Machine Translation and Entity Linking](https://mlsanity.com/paper/2107.02865/) (2021)
- [Spider4SPARQL: A Complex Benchmark for Evaluating Knowledge Graph Question Answering Systems](https://mlsanity.com/paper/2309.16248/) (2023)
- [Conversational Semantic Parsing using Dynamic Context Graphs](https://mlsanity.com/paper/2305.06164/) (2023)
- [Integrating Multi-Head Convolutional Encoders with Cross-Attention for Improved SPARQL Query Translation](https://mlsanity.com/paper/2408.13432/) (2024)
- [Q-NL Verifier: Leveraging Synthetic Data for Robust Knowledge Graph Question Answering](https://mlsanity.com/paper/2503.01385/) (2025)
- [SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2106.09997/) (2021)
- [Knowledge Graph Question Answering via SPARQL Silhouette Generation](https://mlsanity.com/paper/2109.09475/) (2021)
- [Would You Ask it that Way? Measuring and Improving Question Naturalness for Knowledge Graph Question Answering](https://mlsanity.com/paper/2205.12768/) (2022)
- [NeuroQL: A Neuro-Symbolic Language and Dataset for Inter-Subjective Reasoning](https://mlsanity.com/paper/2303.07146/) (2023)
- [PKG API: A Tool for Personal Knowledge Graph Management](https://mlsanity.com/paper/2402.07540/) (2024)
- [Lexicalization Is All You Need: Examining the Impact of Lexical Knowledge in a Compositional QALD System](https://mlsanity.com/paper/2411.03906/) (2024)
- [Automating SPARQL Query Translations between DBpedia and Wikidata](https://mlsanity.com/paper/2507.10045/) (2025)
- [Reducing the impact of out of vocabulary words in the translation of natural language questions into SPARQL queries](https://mlsanity.com/paper/2111.03000/) (2021)
- [KGVoyager: Knowledge Graph Agnostic Question Answering via Agentic Navigation](https://mlsanity.com/paper/2609.01780/) (2026)

## Abstract (arXiv)

SPARQL is a highly powerful query language for an ever-growing number of Linked Data resources and Knowledge Graphs. Using it requires a certain familiarity with the entities in the domain to be queried as well as expertise in the language's syntax and semantics, none of which average human web users can be assumed to possess. To overcome this limitation, automatically translating natural language questions to SPARQL queries has been a vibrant field of research. However, to this date, the vast success of deep learning methods has not yet been fully propagated to this research problem. This paper contributes to filling this gap by evaluating the utilization of eight different Neural Machine Translation (NMT) models for the task of translating from natural language to the structured query language SPARQL. While highlighting the importance of high-quantity and high-quality datasets, the results show a dominance of a CNN-based architecture with a BLEU score of up to 98 and accuracy of up to 94%.