用频率加权增强知识图谱，提升大模型推理能力
BambooKG: A Neurobiologically-inspired Frequency-Weight Knowledge Graph
- 基于神经科学启发，给非三元组边赋予频率权重
- 在单跳和多跳推理任务上超越现有方法
- 适合需要复杂关系推理的问答与知识系统
检索增强生成使大模型能访问外部知识，减少幻觉和数据过时问题。但其独立处理检索片段，难以进行跨文档的多跳或关系推理。知识图谱通过三元组捕捉实体间关系，支持结构化多段推理，但常遗漏不符合三元组结构的信息。本文提出BambooKG，一种基于频率加权的非三元组边知识图谱，借鉴赫布原理‘一起放电，一起连接’，反映链接强度。该设计减少信息丢失，在单跳与多跳推理任务中表现更优，优于现有方案。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation allows LLMs to access external knowledge, reducing hallucinations and ageing-data issues. However, it treats retrieved chunks independently and struggles with multi-hop or relational reasoning, especially across documents. Knowledge graphs enhance this by capturing the relationships between entities using triplets, enabling structured, multi-chunk reasoning. However, these tend to miss information that fails to conform to the triplet structure. We introduce BambooKG, a knowledge graph with frequency-based weights on non-triplet edges which reflect link strength, drawing on the Hebbian principle of "fire together, wire together". This decreases information loss and results in improved performance on single- and multi-hop reasoning, outperforming the existing solutions.
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