CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering
Yike Wu, Yi Huang, Nan Hu, Yuncheng Hua, Guilin Qi, Jiaoyan Chen, Jeff Z. Pan
Abstract
Recent studies have explored the use of Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) for Knowledge Graph Question Answering (KGQA). They typically require rewriting retrieved subgraphs into natural language formats comprehensible to LLMs. However, when tackling complex questions, the knowledge rewritten by existing methods may include irrelevant information, omit crucial details, or fail to align with the question's semantics. To address them, we propose a novel rewriting method CoTKR, Chain-of-Thought Enhanced Knowledge Rewriting, for generating reasoning traces and corresponding knowledge in an interleaved manner, thereby mitigating the limitations of single-step knowledge rewriting. Additionally, to bridge the preference gap between the knowledge rewriter and the question answering (QA) model, we propose a training strategy PAQAF, Preference Alignment from Question Answering Feedback, for leveraging feedback from the QA model to further optimize the knowledge rewriter. We conduct experiments using various LLMs across several KGQA benchmarks. Experimental results demonstrate that, compared with previous knowledge rewriting methods, CoTKR generates the most beneficial knowledge representation for QA models, which significantly improves the performance of LLMs in KGQA 1 . * Equal contribution. †Corresponding author. 1 Our code is available at https://github.com/wuyike2000/CoTKR . Question: what is the unit of area that the measurement system that have an electric field strength units of volt per metre have? Answer: Square kilometer, Square meter KR Methods KR Results Summary The International System of Units includes various measurement systems for different physical quantities such as length, area, volume, conductance, resistance, current, force, and pressure. The measurement system with an electric field strength unit of Volt per meter does not directly relate to a specific area unit within the International System of Units. CoTKR Reason: I need to know the unit of area associated with the measurement system that has an electric field strength unit of Volt per meter. Summarize: The International System of Units includes various units for different physical quantities such as length, area, volume, etc. Reason: I need to find the specific unit of area in the International System of Units that corresponds to the electric field strength unit of Volt per meter. Summarize: The International System of Units includes units like Square kilometer, Square meter, etc. for measuring area. CoTKR+PA Reason: I need to know which measurement system includes the unit for electric field strength in Volt per meter. Knowledge: The International System of Units contains the unit for electric field strength in Volt per meter. Reason: I need to identify the unit of area within the International System of Units. Knowledge: The International System of Units includes the units Square kilometer and Square meter as area units.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 6c50d05f-70d0-4ecf-a25a-e58341b575d3Cited by top-tier papers4
- AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale CorporaJiaxin Bai, Wei Fan, Qi Hu, Qing Zong et al.ACL 2026 · 27 citations
- Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge GraphsJie Ma, Ning Qu, Zhitao Gao, Rui Xing et al.NeurIPS 2025 · 9 citations
- DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented GenerationHanghui Guo, Jia Zhu, Shimin Di, Weijie Shi et al.ACL 2025
- Masking in Multi-hop QA: An Analysis of How Language Models Perform with Context PermutationWenyu Huang, Pavlos Vougiouklis, Mirella Lapata, Jeff Z. PanACL 2025
Builds on17
- Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsJason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma et al.NeurIPS 2022 · 22,562 citations
- Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksPatrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni et al.NeurIPS 2020 · 19,162 citations
- LoRA: Low-Rank Adaptation of Large Language ModelsEdward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu et al.ICLR 2022 · 18,833 citations
- Direct Preference Optimization: Your Language Model is Secretly a Reward ModelRafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D. Manning et al.NeurIPS 2023 · 10,924 citations
- Large Language Models are Zero-Shot ReasonersTakeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo et al.NeurIPS 2022 · 8,168 citations
Related papers
- Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question AnsweringRongzhi Zhu, Xiangyu Liu, Zequn Sun, Yiwei Wang et al.ACL 2025 · 14 citations
- TimeR⁴ : Time-aware Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question AnsweringXinying Qian, Ying Zhang, Yu Zhao, Baohang Zhou et al.EMNLP 2024 · 11 citations
- Improving Complex Knowledge Base Question Answering via Question-to-Action and Question-to-Question AlignmentYechun Tang, Xiaoxia Cheng, Weiming LuEMNLP 2022 · 8 citations
- Plan Then Retrieve: Reinforcement Learning-Guided Complex Reasoning over Knowledge GraphsYanlin Song, Ben Liu, Víctor Gutiérrez-Basulto, Zhiwei Hu et al.WWW 2026
- A Systematic Exploration of Knowledge Graph Alignment with Large Language Models in Retrieval Augmented GenerationShiyu Tian, Shuyue Xing, Xingrui Li, Yangyang Luo et al.AAAI 2025 · 3 citations
