# SPARQL as a Foreign Language

arXiv: https://arxiv.org/abs/1708.07624 · HTML: https://mlsanity.com/paper/1708.07624/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2017.

## Built on by (most cited first, 13 of 13)

- [Modern Baselines for SPARQL Semantic Parsing](https://mlsanity.com/paper/2204.12793/) (2022)
- [A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions](https://mlsanity.com/paper/2304.07772/) (2023)
- [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)
- [Conversational Lexicography: Querying Lexicographic Data on Knowledge Graphs with SPARQL through Natural Language](https://mlsanity.com/paper/2505.19971/) (2025)
- [Integrating Multi-Head Convolutional Encoders with Cross-Attention for Improved SPARQL Query Translation](https://mlsanity.com/paper/2408.13432/) (2024)
- [Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting](https://mlsanity.com/paper/2507.22619/) (2025)
- [LLM-Driven Ontology Construction for Enterprise Knowledge Graphs](https://mlsanity.com/paper/2602.01276/) (2026)
- [SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2106.09997/) (2021)
- [Would You Ask it that Way? Measuring and Improving Question Naturalness for Knowledge Graph Question Answering](https://mlsanity.com/paper/2205.12768/) (2022)
- [Towards Natural Language Question Answering over Earth Observation Linked Data using Attention-based Neural Machine Translation](https://mlsanity.com/paper/2101.09427/) (2021)
- [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)
- [Towards Researcher Agents for Knowledge-Graph Question Answering](https://mlsanity.com/paper/2608.07700/) (2026)

## Abstract (arXiv)

In the last years, the Linked Data Cloud has achieved a size of more than 100 billion facts pertaining to a multitude of domains. However, accessing this information has been significantly challenging for lay users. Approaches to problems such as Question Answering on Linked Data and Link Discovery have notably played a role in increasing information access. These approaches are often based on handcrafted and/or statistical models derived from data observation. Recently, Deep Learning architectures based on Neural Networks called seq2seq have shown to achieve state-of-the-art results at translating sequences into sequences. In this direction, we propose Neural SPARQL Machines, end-to-end deep architectures to translate any natural language expression into sentences encoding SPARQL queries. Our preliminary results, restricted on selected DBpedia classes, show that Neural SPARQL Machines are a promising approach for Question Answering on Linked Data, as they can deal with known problems such as vocabulary mismatch and perform graph pattern composition.