@inproceedings{li-etal-2026-psr,
title = "{PSR}: Efficient Knowledge Graph Question Answering via Plan-Guided Schema-Level Reasoning",
author = "Li, Mingyuan and
Li, Yucheng and
Bao, Yuheng and
Sun, Yawei and
Cheng, Gong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1091/",
pages = "23104--23125",
ISBN = "979-8-89176-462-0",
abstract = "Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) in Knowledge Graph Question Answering (KGQA) by retrieving reasoning paths from external Knowledge Graphs (KGs). However, the practicality of existing methods is often limited by their reliance on iterative retrieval, where the number of required LLM calls increases with retrieval depth, leading to expensive interaction costs. To address this, we propose Plan-guided Schema-level Reasoning (PSR), a training-free and efficient RAG framework for KGQA that requires only three LLM calls. First, PSR generates a retrieval plan from the input question (the first LLM call). It then performs Plan-Regularized Monte Carlo Tree Search to extract a compact set of schema-level paths from the KG, which are subsequently filtered by the LLM (the second LLM call). Finally, PSR grounds the chosen schema-level paths into instance-level reasoning paths to augment the LLM{'}s answer generation (the third LLM call). Experimental results on three datasets demonstrate the high efficiency and competitive accuracy of PSR."
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<abstract>Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) in Knowledge Graph Question Answering (KGQA) by retrieving reasoning paths from external Knowledge Graphs (KGs). However, the practicality of existing methods is often limited by their reliance on iterative retrieval, where the number of required LLM calls increases with retrieval depth, leading to expensive interaction costs. To address this, we propose Plan-guided Schema-level Reasoning (PSR), a training-free and efficient RAG framework for KGQA that requires only three LLM calls. First, PSR generates a retrieval plan from the input question (the first LLM call). It then performs Plan-Regularized Monte Carlo Tree Search to extract a compact set of schema-level paths from the KG, which are subsequently filtered by the LLM (the second LLM call). Finally, PSR grounds the chosen schema-level paths into instance-level reasoning paths to augment the LLM’s answer generation (the third LLM call). Experimental results on three datasets demonstrate the high efficiency and competitive accuracy of PSR.</abstract>
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%0 Conference Proceedings
%T PSR: Efficient Knowledge Graph Question Answering via Plan-Guided Schema-Level Reasoning
%A Li, Mingyuan
%A Li, Yucheng
%A Bao, Yuheng
%A Sun, Yawei
%A Cheng, Gong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F li-etal-2026-psr
%X Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) in Knowledge Graph Question Answering (KGQA) by retrieving reasoning paths from external Knowledge Graphs (KGs). However, the practicality of existing methods is often limited by their reliance on iterative retrieval, where the number of required LLM calls increases with retrieval depth, leading to expensive interaction costs. To address this, we propose Plan-guided Schema-level Reasoning (PSR), a training-free and efficient RAG framework for KGQA that requires only three LLM calls. First, PSR generates a retrieval plan from the input question (the first LLM call). It then performs Plan-Regularized Monte Carlo Tree Search to extract a compact set of schema-level paths from the KG, which are subsequently filtered by the LLM (the second LLM call). Finally, PSR grounds the chosen schema-level paths into instance-level reasoning paths to augment the LLM’s answer generation (the third LLM call). Experimental results on three datasets demonstrate the high efficiency and competitive accuracy of PSR.
%U https://aclanthology.org/2026.findings-emnlp.1091/
%P 23104-23125
Markdown (Informal)
[PSR: Efficient Knowledge Graph Question Answering via Plan-Guided Schema-Level Reasoning](https://aclanthology.org/2026.findings-emnlp.1091/) (Li et al., Findings 2026)
ACL