Neurosymbolic Methods for Dynamic Knowledge Graphs
It addresses the problem of handling evolving knowledge graphs for researchers and practitioners in AI, but is incremental as it reviews existing methods.
This chapter formally defines dynamic knowledge graphs and summarizes their representation, focusing on neurosymbolic methods for dynamic KG completion and entity alignment tasks, while discussing challenges and future directions.
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.