Triple Augmented Generative Language Models for SPARQL Query Generation from Natural Language Questions

Jack Longwell, Mahdiyar Ali Akbar Alavi, Fattane Zarrinkalam, Faezeh Ensan

Abstract: Knowledge Graph Question Answering (KGQA) leverages structured Knowledge Graphs (KG) to respond to Natural Language Questions (NLQ). This paper explores integrating Generative Language Models (GLMs) augmented with knowledge graph triple retrievers into the KGQA framework to generate accurate SPARQL queries from NLQs. Specifically, we evaluate the effectiveness of integrating triple retriever models with the SPARQL-generating capabilities of GLMs by investigating: (1) the standalone capabilities of GLMs independent of retriever performance, (2) the impact of incorporating a base retriever (BM25), and (3) a comparative analysis with state-of-the-art KGQA methods. Our experiments demonstrate that by incorporating a triple retrieval module, GLMs can generate accurate SPARQL queries and outperform current end-to-end KGQA methods, particularly when paired with an optimal retriever.