Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research direction. It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc. According to the graph types, existing KGR models can be roughly divided into three categories, i.e., static models, temporal models, and multi-modal models. Early works in this domain mainly focus on static KGR, and recent works try to leverage the temporal and multi-modal information, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for knowledge graph reasoning tracing from static to temporal and then to multi-modal KGs. Concretely, the models are reviewed based on bi-level taxonomy, i.e., top-level (graph types) and base-level (techniques and scenarios). Besides, the performances, as well as datasets, are summarized and presented. Moreover, we point out the challenges and potential opportunities to enlighten the readers. The corresponding open-source repository is shared on GitHub https://github.com/LIANGKE23/Awesome-Knowledge-Graph-Reasoning.

翻译：知识图谱推理（KGR）旨在基于挖掘的知识图谱（KG）逻辑规则，从既有事实中推导出新事实，已成为快速发展的研究方向。大量证据表明，该方法能显著提升知识图谱在问答系统、推荐系统等人工智能应用中的效用。根据图类型差异，现有KGR模型可大致分为三类：静态模型、时序模型与多模态模型。早期研究主要聚焦静态KGR，近期工作则致力于利用更贴近实际场景的时序与多模态信息。然而，现有综述论文与开源存储库尚未对该重要方向进行全面总结与讨论。为填补这一空白，我们率先对从静态知识图谱到时序化、多模态知识图谱的知识图谱推理展开综述。具体而言，本综述基于双层分类体系（顶层按图类型划分，底层按技术与场景划分）对模型进行系统梳理，同时总结呈现模型性能及数据集。此外，我们指出了当前挑战与潜在机遇，以期为读者提供启示。对应的开源存储库已发布于GitHub网站：https://github.com/LIANGKE23/Awesome-Knowledge-Graph-Reasoning。