GRISP: Guided Recurrent IRI Selection over SPARQL Skeletons

• URL: http://arxiv.org/abs/2604.21133v1
• Date: Wed, 22 Apr 2026 22:47:01 GMT
• Title: GRISP: Guided Recurrent IRI Selection over SPARQL Skeletons
• Abstract summary: We present a novel SPARQL-based question-answering method over knowledge graphs based on fine-tuning a small language model (SLM)<n>We evaluate the method on common Wikidata and Freebase benchmarks, and achieve better results than other state-of-the-art methods in a comparable setting.
• Score: 2.9907607782169543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present GRISP (Guided Recurrent IRI Selection over SPARQL Skeletons), a novel SPARQL-based question-answering method over knowledge graphs based on fine-tuning a small language model (SLM). Given a natural-language question, the method first uses the SLM to generate a natural-language SPARQL query skeleton, and then to re-rank and select knowledge graph items to iteratively replace the natural-language placeholders using knowledge graph constraints. The SLM is jointly trained on skeleton generation and list-wise re-ranking data generated from standard question-query pairs. We evaluate the method on common Wikidata and Freebase benchmarks, and achieve better results than other state-of-the-art methods in a comparable setting.

Related papers

• SPARQL-LLM: Real-Time SPARQL Query Generation from Natural Language Questions [1.3856736555085554]
  SPARQL-LLM is an open-source and triplestore-agnostic approach, powered by lightweight metadata, that generates SPARQL queries from natural language text.<n>We show that SPARQL-LLM is up to 36x faster than other systems participating in the challenge, while costing a maximum of $0.01 per question.
  arXiv  Detail & Related papers  (2025-12-16T10:39:46Z)
• FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs [0.5120567378386615]
  We propose a modular framework that supports fine-tuned LLMs as a core component, with optional context provided via RAG and a SPARQL query correction layer.<n>We measure query accuracy using BLEU and ROUGE metrics, and query result accuracy using relaxed exact match(RelaxedEM)<n> Experimental results demonstrate that fine-tuning achieves the highest overall performance, reaching 0.90 ROUGE-L for query accuracy and 0.85 RelaxedEM for result accuracy on the test set.
  arXiv  Detail & Related papers  (2025-08-14T09:08:50Z)
• Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning [51.203811759364925]
  mKGQAgent breaks down the task of converting natural language questions into SPARQL queries into modular, interpretable subtasks.<n> Evaluated on the DBpedia- and Corporate-based KGQA benchmarks within the Text2SPARQL challenge 2025, our approach took first place among the other participants.
  arXiv  Detail & Related papers  (2025-07-22T19:23:03Z)
• SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injection [81.78173888579941]
  Large Language Models (LLMs) are considered a well-suited method to increase the quality of the question-answering functionality.<n>LLMs are trained on web data, where researchers have no control over whether the benchmark or the knowledge graph was already included in the training data.<n>This paper introduces a novel method that evaluates the quality of LLMs by generating a SPARQL query from a natural-language question.
  arXiv  Detail & Related papers  (2025-07-18T12:28:08Z)
• GRASP: Generic Reasoning And SPARQL Generation across Knowledge Graphs [4.005483185111992]
  We propose a new approach for generating SPARQL queries on RDF knowledge graphs from natural language questions or keyword queries.<n>Our approach does not require fine-tuning. Instead, it uses the language model to explore the knowledge graph by strategically executing SPARQL queries and searching for relevant IRIs and literals.
  arXiv  Detail & Related papers  (2025-07-10T18:50:05Z)
• Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling [69.84963245729826]
  We propose an auxiliary task of QL to enhance the backbone for subsequent contrastive learning of the retriever.<n>We introduce our model, which incorporates two key components: Attention Block (AB) and Document Corruption (DC)
  arXiv  Detail & Related papers  (2025-04-07T16:03:59Z)
• Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA [51.3033125256716]
  We model the subgraph retrieval task as a conditional generation task handled by small language models. Our base generative subgraph retrieval model, consisting of only 220M parameters, competitive retrieval performance compared to state-of-the-art models. Our largest 3B model, when plugged with an LLM reader, sets new SOTA end-to-end performance on both the WebQSP and CWQ benchmarks.
  arXiv  Detail & Related papers  (2024-10-08T15:22:36Z)
• An In-Context Schema Understanding Method for Knowledge Base Question Answering [70.87993081445127]
  Large Language Models (LLMs) have shown strong capabilities in language understanding and can be used to solve this task. Existing methods bypass this challenge by initially employing LLMs to generate drafts of logic forms without schema-specific details. We propose a simple In-Context Understanding (ICSU) method that enables LLMs to directly understand schemas by leveraging in-context learning.
  arXiv  Detail & Related papers  (2023-10-22T04:19:17Z)
• A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions [2.5782420501870296]
  In recent years, the field of neural machine translation (NMT) for SPARQL query generation has witnessed significant growth. This paper presents various experiments that replicate and expand upon recent NMT-based SPARQL generation studies.
  arXiv  Detail & Related papers  (2023-04-16T13:12:26Z)
• Exploring Sequence-to-Sequence Models for SPARQL Pattern Composition [0.5639451539396457]
  A booming amount of information is continuously added to the Internet as structured and unstructured data, feeding knowledge bases such as DBpedia and Wikidata. The aim of Question Answering systems is to allow lay users to access such data using natural language without needing to write formal queries. We show that sequence-to-sequence models are a viable and promising option to transform long utterances into complex SPARQL queries.
  arXiv  Detail & Related papers  (2020-10-21T11:12:01Z)
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks [133.93803565077337]
  retrieval-augmented generation models combine pre-trained parametric and non-parametric memory for language generation. We show that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.
  arXiv  Detail & Related papers  (2020-05-22T21:34:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.