A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multi-Modal
Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu†, , Fuchun Sun † Corresponding Author. Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, and Xinwang Liu are with the School of Computer, National University of Defense Technology, Changsha, 410073, China. E-mail: Sihang Zhou is with the College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China. Fuchun Sun is with the Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China.This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
Abstract
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 potent
中文速览
知识图谱（Knowledge Graph, KG）中存在大量缺失事实，如何从已有知识中自动推断出新知识，是知识图谱推理（Knowledge Graph Reasoning, KGR）领域的核心挑战。这篇论文系统梳理了横跨静态、时序和多模态三类知识图谱的180余个推理模型，提出了一套"双层分类体系"——顶层按图谱类型划分，底层按推理技术和推理场景细分，并对各类模型的性能表现和67个常用数据集进行了汇总比较。这是首篇将静态、时序与多模态知识图谱推理统一纳入同一综述框架的工作，填补了该方向系统性总结的空白。作者还整理了开源代码库并指出了当前面临的挑战与未来机遇，为研究者选择基线方法、把握前沿方向提供了实用指引。
原文 arXiv:2212.05767;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2212.05767v7