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 and recommendation systems, etc. According to the graph types, the existing KGR models can be roughly divided into three categories, i.e., static models, temporal models, and multi-modal models. The early works in this domain mainly focus on static KGR and tend to directly apply general knowledge graph embedding models to the reasoning task. However, these models are not suitable for more complex but practical tasks, such as inductive static KGR, temporal KGR, and multi-modal KGR. To this end, multiple works have been developed recently, but no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a survey for knowledge graph reasoning tracing from static to temporal and then to multi-modal KGs. Concretely, the preliminaries, summaries of KGR models, and typical datasets are introduced and discussed consequently. Moreover, we discuss the challenges and potential opportunities. 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），旨在基于知识图谱（KG）中挖掘的逻辑规则，从现有事实中推导出新事实，已成为一个快速发展的研究方向。该技术已被证明能显著提升知识图谱在诸多人工智能应用中的使用效果，例如问答系统和推荐系统等。根据图类型的不同，现有KGR模型可大致分为三类，即静态模型、时序模型和多模态模型。该领域的早期工作主要关注静态KGR，倾向于将通用知识图谱嵌入模型直接应用于推理任务。然而，这些模型并不适用于更复杂但更具实用性的任务，例如归纳式静态KGR、时序KGR和多模态KGR。为此，近年来已有多个研究成果被提出，但目前尚无综述性论文和开源代码库全面总结并讨论这一重要方向的研究进展。为填补这一空白，我们撰写了这篇综述文章，对知识图谱推理从静态到时序再到多模态知识图谱的发展脉络进行了梳理。具体而言，本文依次介绍并讨论了相关基础概念、KGR模型总结以及典型数据集。此外，我们还探讨了当前面临的挑战与潜在机遇。对应的开源代码库已发布在GitHub上：https://github.com/LIANGKE23/Awesome-Knowledge-Graph-Reasoning。