Shixuan Liu , Haoxiang Cheng , Yunfei Wang , Yue He , Changjun Fan , Zhong Liu
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引用次数: 0
摘要
异构信息网络(HIN)封装了各种实体和关系类型,元路径为知识推理提供了重要的元级语义,但其实用性受到发现挑战的限制。虽然大语言模型(LLM)因其广泛的知识编码和高效性为元路径发现提供了新的前景,但其适应性面临着语料偏差、词汇差异和幻觉等挑战。EvoPath 是一种利用 LLMs 高效识别高质量元路径的创新框架,本文通过介绍 EvoPath 率先缓解了这些挑战。EvoPath 经过精心设计,每个组件都旨在解决可能导致潜在知识冲突的问题。EvoPath 使用最小的 HIN 事实子集,通过动态重放缓冲区中的元路径,并根据其分数确定优先级,从而迭代生成和演化元路径。在三个包含数百个关系的大型复杂 HIN 上进行的综合实验证明,我们的框架 EvoPath 能够通过有效的提示使 LLM 生成高质量的元路径,从而证实了它在 HIN 推理任务中的卓越性能。进一步的消融研究验证了该框架中每个模块的有效性。
EvoPath: Evolutionary meta-path discovery with large language models for complex heterogeneous information networks
Heterogeneous Information Networks (HINs) encapsulate diverse entity and relation types, with meta-paths providing essential meta-level semantics for knowledge reasoning, although their utility is constrained by discovery challenges. While Large Language Models (LLMs) offer new prospects for meta-path discovery due to their extensive knowledge encoding and efficiency, their adaptation faces challenges such as corpora bias, lexical discrepancies, and hallucination. This paper pioneers the mitigation of these challenges by presenting EvoPath, an innovative framework that leverages LLMs to efficiently identify high-quality meta-paths. EvoPath is carefully designed, with each component aimed at addressing issues that could lead to potential knowledge conflicts. With a minimal subset of HIN facts, EvoPath iteratively generates and evolves meta-paths by dynamically replaying meta-paths in the buffer with prioritization based on their scores. Comprehensive experiments on three large, complex HINs with hundreds of relations demonstrate that our framework, EvoPath, enables LLMs to generate high-quality meta-paths through effective prompting, confirming its superior performance in HIN reasoning tasks. Further ablation studies validate the effectiveness of each module within the framework.
期刊介绍:
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