# Integrating Multi-Head Convolutional Encoders with Cross-Attention for Improved SPARQL Query Translation

arXiv: https://arxiv.org/abs/2408.13432 · HTML: https://mlsanity.com/paper/2408.13432/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 24 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [Leveraging Abstract Meaning Representation for Knowledge Base Question Answering](https://mlsanity.com/paper/2012.01707/) (2020)
- [Modern Baselines for SPARQL Semantic Parsing](https://mlsanity.com/paper/2204.12793/) (2022)
- [Message Passing for Complex Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1908.06917/) (2019)
- [Neural Machine Translating from Natural Language to SPARQL](https://mlsanity.com/paper/1906.09302/) (2019)
- [A Universal Question-Answering Platform for Knowledge Graphs](https://mlsanity.com/paper/2303.00595/) (2023)
- [Towards a Question Answering System over the Semantic Web](https://mlsanity.com/paper/1803.00832/) (2018)
- [SPARQL as a Foreign Language](https://mlsanity.com/paper/1708.07624/) (2017)
- [Question Answering over Knowledge Graphs with Neural Machine Translation and Entity Linking](https://mlsanity.com/paper/2107.02865/) (2021)

## Built on by (most cited first, 2 of 2)

- [KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation](https://mlsanity.com/paper/2409.13731/) (2024)
- [Towards Researcher Agents for Knowledge-Graph Question Answering](https://mlsanity.com/paper/2608.07700/) (2026)

## Abstract (arXiv)

The main task of the KGQA system (Knowledge Graph Question Answering) is to convert user input questions into query syntax (such as SPARQL). With the rise of modern popular encoders and decoders like Transformer and ConvS2S, many scholars have shifted the research direction of SPARQL generation to the Neural Machine Translation (NMT) architecture or the generative AI field of Text-to-SPARQL. In NMT-based QA systems, the system treats knowledge base query syntax as a language. It uses NMT-based translation models to translate natural language questions into query syntax. Scholars use popular architectures equipped with cross-attention, such as Transformer, ConvS2S, and BiLSTM, to train translation models for query syntax. To achieve better query results, this paper improved the ConvS2S encoder and added multi-head attention from the Transformer, proposing a Multi-Head Conv encoder (MHC encoder) based on the n-gram language model. The principle is to use convolutional layers to capture local hidden features in the input sequence with different receptive fields, using multi-head attention to calculate dependencies between them. Ultimately, we found that the translation model based on the Multi-Head Conv encoder achieved better performance than other encoders, obtaining 76.52% and 83.37% BLEU-1 (BiLingual Evaluation Understudy) on the QALD-9 and LC-QuAD-1.0 datasets, respectively. Additionally, in the end-to-end system experiments on the QALD-9 and LC-QuAD-1.0 datasets, we achieved leading results over other KGQA systems, with Macro F1-measures reaching 52% and 66%, respectively. Moreover, the experimental results show that with limited computational resources, if one possesses an excellent encoder-decoder architecture and cross-attention, experts and scholars can achieve outstanding performance equivalent to large pre-trained models using only general embeddings.