GRISP: Guided Recurrent IRI Selection over SPARQL Skeletons

Abstract:We present GRISP (Guided Recurrent IRI Selection over SPARQL Skeletons), a novel SPARQL-based question-answering method over knowledge graphs using a fine-tuned small language model (SLM). Given a natural-language question, the method uses the SLM to generate a natural-language SPARQL query skeleton, and then resolves the natural-language placeholders using a backtracking search that retrieves and re-ranks knowledge graph items under knowledge graph constraints. The SLM is jointly trained on skeleton generation and re-ranking data generated from question-query pairs. We evaluate the method on common Wikidata and Freebase benchmarks, and achieve better results than other state-of-the-art fine-tuning-based methods.

Computation and Language (cs.CL)

arXiv:2604.21133 [cs.CL]

(or arXiv:2604.21133v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2604.21133 Focus to learn more arXiv-issued DOI via DataCite

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