Neurosymbolic Methods for Dynamic Knowledge Graphs

TL;DR

This paper reviews neurosymbolic methods tailored for dynamic knowledge graphs, emphasizing their application in tasks like KG completion and entity alignment, and discusses challenges and future research directions.

Contribution

It provides a comprehensive overview of neurosymbolic approaches for dynamic KGs, highlighting recent methods and identifying open challenges in the field.

Findings

Neurosymbolic methods improve dynamic KG completion.

Temporal information enhances entity alignment.

Current approaches face scalability and temporal reasoning challenges.

Abstract

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.