标题： 异质图增强的链式思维LLM推理用于学术问答 显示英文标题

标题： HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering

摘要： 在异构学术网络中的学术问答（QA）提出了独特的挑战，需要结构化理解和可解释推理。 虽然图神经网络（GNNs）能够捕捉结构化的图信息，大型语言模型（LLMs）在语义理解方面表现出强大的能力，但当前方法在推理层面缺乏整合。 我们提出了HetGCoT框架，使LLMs能够有效地利用和从图中学习信息以推理出可解释的学术QA结果。 我们的框架引入了三个技术贡献：（1）一种将异构图结构信息转换为LLM可处理的推理链的框架，（2）一种自适应元路径选择机制，用于识别特定查询的相关子图，以及（3）一种多步推理策略，系统地将图上下文纳入推理过程。 在OpenAlex和DBLP数据集上的实验表明，我们的方法优于所有最先进的基线。 该框架在不同的LLM架构上表现出适应性，并适用于各种学术问答任务。 显示英文摘要

摘要： Academic question answering (QA) in heterogeneous scholarly networks presents unique challenges requiring both structural understanding and interpretable reasoning. While graph neural networks (GNNs) capture structured graph information and large language models (LLMs) demonstrate strong capabilities in semantic comprehension, current approaches lack integration at the reasoning level. We propose HetGCoT, a framework enabling LLMs to effectively leverage and learn information from graphs to reason interpretable academic QA results. Our framework introduces three technical contributions: (1) a framework that transforms heterogeneous graph structural information into LLM-processable reasoning chains, (2) an adaptive metapath selection mechanism identifying relevant subgraphs for specific queries, and (3) a multi-step reasoning strategy systematically incorporating graph contexts into the reasoning process. Experiments on OpenAlex and DBLP datasets show our approach outperforms all sota baselines. The framework demonstrates adaptability across different LLM architectures and applicability to various scholarly question answering tasks.