@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://aclanthology.org/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."
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<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 (Subgraph-Guided Executable Logical Form 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.</abstract>
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%0 Conference Proceedings
%T Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering
%A Tian, Yuhang
%A Song, Dandan
%A Wu, Zhijing
%A Zhou, Changzhi
%A Yang, Jun
%A Ma, Huipeng
%A Li, Chenhao
%A Zhang, Luan
%A Li, Yading
%A Li, Xudong
%A Liu, Shenxi
%A Jiang, Jing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F tian-etal-2026-subgraph
%X 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 (Subgraph-Guided Executable Logical Form 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.
%U https://aclanthology.org/2026.findings-acl.177/
%P 3614-3635
Markdown (Informal)
[Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering](https://aclanthology.org/2026.findings-acl.177/) (Tian et al., Findings 2026)
ACL
- Yuhang Tian, Dandan Song, Zhijing Wu, Changzhi Zhou, Jun Yang, Huipeng Ma, Chenhao Li, Luan Zhang, Yading Li, Xudong Li, Shenxi Liu, and Jing Jiang. 2026. Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering. In Findings of the Association for Computational Linguistics: ACL 2026, pages 3614–3635, San Diego, California, United States. Association for Computational Linguistics.