Towards Self-cognitive Exploration: Metacognitive Knowledge Graph Retrieval Augmented Generation
Xujie Yuan, Shimin Di, Jielong Tang, Libin Zheng, Jian Yin

TL;DR
This paper introduces MetaKGRAG, a novel metacognitive framework for knowledge graph retrieval that enables self-assessment and path-aware refinement, significantly improving reasoning accuracy in large language models across multiple domains.
Contribution
It proposes a new closed-loop, path-aware refinement framework inspired by human metacognition, addressing cognitive blindness in KG-RAG systems.
Findings
MetaKGRAG outperforms existing KG-RAG methods on five datasets.
The framework effectively identifies and corrects exploration deficiencies.
Results highlight the importance of path-aware self-refinement in structured knowledge retrieval.
Abstract
Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) significantly enhances the reasoning capabilities of LargeLanguage Models by leveraging structured knowledge. However, existing KG-RAG frameworks typically operate as open-loop systems, suffering from cognitive blindness, an inability to recognize their exploration deficiencies. This leads to relevance drift and incomplete evidence, which existing self-refinement methods, designed for unstructured text-based RAG, cannot effectively resolve due to the path-dependent nature of graph exploration. To address this challenge, we propose Metacognitive Knowledge Graph Retrieval Augmented Generation (MetaKGRAG), a novel framework inspired by the human metacognition process, which introduces a Perceive-Evaluate-Adjust cycle to enable path-aware, closed-loop refinement. This cycle empowers the system to self-assess exploration quality,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
