Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, Knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolving by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to unify LLMs and KGs together and simultaneously leverage their advantages. In this article, we present a forward-looking roadmap for the unification of LLMs and KGs. Our roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs, which incorporate KGs during the pre-training and inference phases of LLMs, or for the purpose of enhancing understanding of the knowledge learned by LLMs; 2) LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding, completion, construction, graph-to-text generation, and question answering; and 3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a mutually beneficial way to enhance both LLMs and KGs for bidirectional reasoning driven by both data and knowledge. We review and summarize existing efforts within these three frameworks in our roadmap and pinpoint their future research directions.

翻译：大语言模型（如ChatGPT和GPT4）凭借其涌现能力和泛化性，正在自然语言处理与人工智能领域掀起新浪潮。然而，大语言模型是黑盒模型，往往难以有效捕获和访问事实性知识。相比之下，知识图谱（例如维基百科和华为知识图谱）作为结构化知识模型，显式存储了丰富的事实性知识。知识图谱可通过为推理和可解释性提供外部知识来增强大语言模型，但知识图谱本身构建困难且具有动态演化特性，这使得现有知识图谱方法在生成新事实和表征未知知识方面面临挑战。因此，将大语言模型与知识图谱统一并同时发挥二者优势具有互补性。本文提出面向大语言模型与知识图谱统一的远景路线图，包含三大通用框架：1）知识图谱增强大语言模型，即在预训练和推理阶段融入知识图谱，或用于增强对大语言模型所学知识的理解；2）大语言模型增强知识图谱，即利用大语言模型完成知识图谱嵌入、补全、构建、图到文本生成及问答等任务；3）协同大语言模型+知识图谱，其中大语言模型与知识图谱发挥同等作用，通过数据与知识驱动的双向推理实现互利共赢。我们在路线图中综述了现有研究并指明未来方向。