GRISP: Guided Recurrent IRI Selection over SPARQL Skeletons
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 f...
| Authors | Sebastian Walter, Hannah Bast |
|---|---|
| Primary category | cs.CL |
| Published | 22 April 2026 |
| Last updated at source | 27 July 2026 |
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