用小模型生成SPARQL骨架，再精准填充实体，提升知识图谱问答准确率。
GRISP: Guided Recurrent IRI Selection over SPARQL Skeletons
- 先用小模型生成带空位的SPARQL查询框架
- 通过回溯搜索在知识图谱中找回并重排序实体
- 联合训练生成与重排，适合复杂问答任务
我们提出GRISP（基于SPARQL骨架的引导式递归IRI选择），一种基于微调小语言模型（SLM）的SPARQL问答方法。给定自然语言问题，该方法利用SLM生成包含自然语言占位符的SPARQL查询骨架，随后通过回溯搜索，在知识图谱约束下检索并重排序对应实体。SLM在问题-查询对生成的骨架生成与重排数据上进行联合训练。我们在Wikidata和Freebase常见基准上评估，结果优于其他最先进的微调方法。
原文摘要 · Abstract (English)
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.
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