@inproceedings{song-etal-2025-phrases,
    title = "From Phrases to Subgraphs: Fine-Grained Semantic Parsing for Knowledge Graph Question Answering",
    author = "Song, Yurun  and
      Shen, Xiangqing  and
      Xia, Rui",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.272/",
    pages = "5232--5246",
    ISBN = "979-8-89176-256-5",
    abstract = "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/From-Phrases-to-Subgraphs."
}