From Phrases to Subgraphs: Fine-Grained Semantic Parsing for Knowledge Graph Question Answering

The recent emergence of large language models (LLMs) has brought new opportunities to knowledge graph question answering (KGQA), but also introduces challenges such as semantic misalignment and reasoning noise.Semantic parsing (SP), previously a mainstream approach for KGQA, enables precise graph pattern matching by mapping natural language queries to executable logical forms.However, it faces limitations in scalability and generalization, especially when dealing with complex, multi-hop reasoning tasks.In this work, we propose a Fine-Grained Semantic Parsing (FGSP) framework for KGQA.Our framework constructs a fine-grained mapping library via phrase-level segmentation of historical question-logical form pairs, and performs online retrieval and fusion of relevant subgraph fragments to answer complex queries.This fine-grained, compositional approach ensures tighter semantic alignment between questions and knowledge graph structures, enhancing both interpretability and adaptability to diverse query types.Experimental results on two KGQA benchmarks demonstrate the effectiveness of FGSP, with a notable 18.5% relative F1 performance improvement over the SOTA on the complex multi-hop CWQ dataset.Our code is available at https://github.com/NUSTM/