标题： EvoPath：用于复杂异构信息网络的大型语言模型进化元路径发现 显示英文标题

标题： EvoPath: Evolutionary Meta-path Discovery with Large Language Models for Complex Heterogeneous Information Networks

摘要： 异构信息网络（HINs）包含多种实体和关系类型，其中元路径为知识推理提供了重要的元级语义，尽管其效用受到发现挑战的限制。 虽然大型语言模型（LLMs）由于其广泛的知识编码和效率，为元路径发现提供了新的前景，但其适应面临语料库偏差、词汇差异和幻觉等挑战。 本文通过提出EvoPath，一种创新的框架，率先缓解这些挑战，该框架利用LLMs高效地识别高质量的元路径。 EvoPath经过精心设计，每个组件旨在解决可能导致潜在知识冲突的问题。 在少量HIN事实的基础上，EvoPath通过动态重放缓冲区中的元路径并根据其得分进行优先排序，迭代生成和演化元路径。 在三个具有数百种关系的大规模、复杂的HIN上的全面实验表明，我们的框架EvoPath通过有效的提示使LLMs生成高质量的元路径，证实了其在HIN推理任务中的优越性能。 进一步的消融研究验证了框架内每个模块的有效性。 显示英文摘要

摘要： 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.