融合符号与神经网络，解决动态知识图谱的补全与对齐问题
Neurosymbolic Methods for Dynamic Knowledge Graphs
- 结合神经网络与符号推理，处理随时间变化的知识图谱
- 支持带时序和不带时序的动态知识图谱补全与实体对齐
- 适合研究知识图谱演化、动态推理的学者参考
知识图谱（KG）在诸多工具与应用中被广泛使用，已成为结构化信息的重要资源。然而在真实世界中，知识图谱会因新实体与关系的不断加入而持续增长，呈现动态特性。本章形式化定义了多种动态知识图谱类型，并总结其表示方法。此外，已有大量神经符号方法被提出用于静态知识图谱的表示学习，涵盖知识图谱补全与实体对齐等任务。本章进一步聚焦于带有或不带时间信息的动态知识图谱的神经符号方法，深入探讨其在动态（时序或非时序）知识图谱补全与实体对齐任务中的应用。同时，分析了现有方法面临的挑战，并指明未来的研究方向。
原文摘要 · Abstract (English)
Knowledge graphs (KGs) have recently been used for many tools and applications, making them rich resources in structured format. However, in the real world, KGs grow due to the additions of new knowledge in the form of entities and relations, making these KGs dynamic. This chapter formally defines several types of dynamic KGs and summarizes how these KGs can be represented. Additionally, many neurosymbolic methods have been proposed for learning representations over static KGs for several tasks such as KG completion and entity alignment. This chapter further focuses on neurosymbolic methods for dynamic KGs with or without temporal information. More specifically, it provides an insight into neurosymbolic methods for dynamic (temporal or non-temporal) KG completion and entity alignment tasks. It further discusses the challenges of current approaches and provides some future directions.
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