Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning

TL;DR

This paper presents mKGQAgent, a human-inspired, modular framework that converts multilingual natural language questions into SPARQL queries, achieving top results in KGQA benchmarks by mimicking human reasoning processes.

Contribution

The paper introduces a novel modular framework using a coordinated LLM workflow for multilingual KGQA, emphasizing interpretability and human-like reasoning, outperforming existing methods.

Findings

Achieved first place in Text2SPARQL challenge 2025 benchmarks.

Demonstrated effective handling of multilingual questions in KGQA.

Showcased the benefits of modular, human-inspired reasoning in semantic parsing.

Abstract

Accessing knowledge via multilingual natural-language interfaces is one of the emerging challenges in the field of information retrieval and related ones. Structured knowledge stored in knowledge graphs can be queried via a specific query language (e.g., SPARQL). Therefore, one needs to transform natural-language input into a query to fulfill an information need. Prior approaches mostly focused on combining components (e.g., rule-based or neural-based) that solve downstream tasks and come up with an answer at the end. We introduce mKGQAgent, a human-inspired framework that breaks down the task of converting natural language questions into SPARQL queries into modular, interpretable subtasks. By leveraging a coordinated LLM agent workflow for planning, entity linking, and query refinement - guided by an experience pool for in-context learning - mKGQAgent efficiently handles multilingual KGQA. Evaluated on the DBpedia- and Corporate-based KGQA benchmarks within the Text2SPARQL challenge 2025, our approach took first place among the other participants. This work opens new avenues for developing human-like reasoning systems in multilingual semantic parsing.