# Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention

arXiv: https://arxiv.org/abs/1803.04329 · HTML: https://mlsanity.com/paper/1803.04329/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2018.

## Built on by (most cited first, 8 of 8)

- [A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions](https://mlsanity.com/paper/2304.07772/) (2023)
- [Financial data analysis application via multi-strategy text processing](https://mlsanity.com/paper/2204.11394/) (2022)
- [UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Models](https://mlsanity.com/paper/2406.02110/) (2024)
- [Question Answering over Knowledge Graphs with Neural Machine Translation and Entity Linking](https://mlsanity.com/paper/2107.02865/) (2021)
- [Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting](https://mlsanity.com/paper/2507.22619/) (2025)
- [SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2106.09997/) (2021)
- [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)

## Abstract (arXiv)

Semantic parsing is the process of mapping a natural language sentence into a formal representation of its meaning. In this work we use the neural network approach to transform natural language sentence into a query to an ontology database in the SPARQL language. This method does not rely on handcraft-rules, high-quality lexicons, manually-built templates or other handmade complex structures. Our approach is based on vector space model and neural networks. The proposed model is based in two learning steps. The first step generates a vector representation for the sentence in natural language and SPARQL query. The second step uses this vector representation as input to a neural network (LSTM with attention mechanism) to generate a model able to encode natural language and decode SPARQL.