Despite their competitive performance on knowledge-intensive tasks, large language models (LLMs) still have limitations in memorizing all world knowledge especially long tail knowledge. In this paper, we study the KG-augmented language model approach for solving the knowledge graph question answering (KGQA) task that requires rich world knowledge. Existing work has shown that retrieving KG knowledge to enhance LLMs prompting can significantly improve LLMs performance in KGQA. However, their approaches lack a well-formed verbalization of KG knowledge, i.e., they ignore the gap between KG representations and textual representations. To this end, we propose an answer-sensitive KG-to-Text approach that can transform KG knowledge into well-textualized statements most informative for KGQA. Based on this approach, we propose a KG-to-Text enhanced LLMs framework for solving the KGQA task. Experiments on several KGQA benchmarks show that the proposed KG-to-Text augmented LLMs approach outperforms previous KG-augmented LLMs approaches regarding answer accuracy and usefulness of knowledge statements.

翻译：尽管大语言模型在知识密集型任务上表现出色，但其在记忆所有世界知识（尤其是长尾知识）方面仍存在局限性。本文研究基于知识图谱增强的语言模型方法，以解决需要丰富世界知识的知识图谱问答任务。现有研究表明，通过检索知识图谱知识来增强大语言模型的提示，可显著提升其在知识图谱问答中的性能。然而，这些方法缺乏对知识图谱知识的规范化语言表达，即忽略了知识图谱表示与文本表示之间的语义鸿沟。为此，我们提出一种答案感知的知识图谱到文本方法，能将知识图谱知识转化为对知识图谱问答最具信息量的规范化文本表述。基于该方法，我们构建了一个面向知识图谱问答的KG到文本增强型大语言模型框架。在多个知识图谱问答基准上的实验表明，相比以往的知识图谱增强型大语言模型方法，本文提出的KG到文本增强型大语言模型方法在答案准确性和知识语句有用性方面均展现出更优性能。