KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models

2024-12-07

arXiv.org

AI Summary

To address information fragmentation and cross-document reasoning challenges in complex retrieval tasks such as multi-hop question answering, this paper proposes HierRAG, a knowledge graph–driven hierarchical retrieval-augmented framework. HierRAG constructs a layered index graph integrating a knowledge graph layer and a collaborative document layer, leveraging graph neural networks to jointly model entity–document relationships—enabling coordinated coarse-grained semantic navigation and fine-grained knowledge localization. Unlike conventional flat RAG architectures, HierRAG introduces the first hierarchical indexing structure, significantly improving intra- and inter-document connectivity and multi-hop reasoning capability. Evaluated on five mainstream multi-hop QA benchmarks, HierRAG achieves substantial gains in both retrieval accuracy and response efficiency, demonstrating its effectiveness and generalizability in complex reasoning scenarios.

Technology Category

Knowledge Representation and Reasoning: Computational Complexity of ReasoningData Mining & Knowledge Management: Conversational Systems for Recommendation & RetrievalNatural Language Processing: Question Answering

Application Category

Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAGGraph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large language models with retrieval-augmented generation encounter a pivotal challenge in intricate retrieval tasks, e.g., multi-hop question answering, which requires the model to navigate across multiple documents and generate comprehensive responses based on fragmented information. To tackle this challenge, we introduce a novel Knowledge Graph-based RAG framework with a hierarchical knowledge retriever, termed KG-Retriever. The retrieval indexing in KG-Retriever is constructed on a hierarchical index graph that consists of a knowledge graph layer and a collaborative document layer. The associative nature of graph structures is fully utilized to strengthen intra-document and inter-document connectivity, thereby fundamentally alleviating the information fragmentation problem and meanwhile improving the retrieval efficiency in cross-document retrieval of LLMs. With the coarse-grained collaborative information from neighboring documents and concise information from the knowledge graph, KG-Retriever achieves marked improvements on five public QA datasets, showing the effectiveness and efficiency of our proposed RAG framework.

Problem

• Enhances multi-hop question answering in retrieval-augmented LLMs
• Addresses information fragmentation via hierarchical knowledge indexing
• Improves cross-document retrieval efficiency using graph structures

Innovation

• Hierarchical knowledge retriever for RAG
• Graph-based index for document connectivity
• Combines knowledge graph and document layers