@inproceedings{tian-etal-2026-subgraph,
    title = "Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering",
    author = "Tian, Yuhang  and
      Song, Dandan  and
      Wu, Zhijing  and
      Zhou, Changzhi  and
      Yang, Jun  and
      Ma, Huipeng  and
      Li, Chenhao  and
      Zhang, Luan  and
      Li, Yading  and
      Li, Xudong  and
      Liu, Shenxi  and
      Jiang, Jing",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.177/",
    pages = "3614--3635",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) have shown great potential in Knowledge Base Question Answering (KBQA) via semantic parsing. However, existing retrieval-augmented approaches typically retrieve entities and relations in isolation based solely on semantic similarity, ignoring the structural information of the Knowledge Base (KB) and the question. To address this limitation, we propose SELF-KBQA (\textbf{S}ubgraph-Guided \textbf{E}xecutable \textbf{L}ogical \textbf{F}orm Generation), a novel framework that empowers LLMs to generate logical forms conditioned on structurally aligned and semantically relevant subgraphs. Specifically, we introduce a structure-aware subgraph retrieval stage that ranks candidate subgraphs by aligning them with the question{'}s structure, along with semantic relevance. Subsequently, we employ a token-budgeted evidence condensation strategy to distill the top-ranked subgraphs into compact contexts for the generation stage. Extensive experiments on GrailQA, WebQSP, and GraphQuestions demonstrate that SELF-KBQA achieves state-of-the-art performance."
}