Representation learning for knowledge fusion and reasoning in Cyber-Physical-Social Systems: Survey and perspectives

The digital deep integration of cyber space, physical space and social space facilitates the formation of Cyber-Physical-Social Systems (CPSS). Knowledge empowers CPSS to be capable of solving complex tasks and providing intelligent services. Knowledge representation, fusion, and reasoning are complementary in the knowledge graph and worthy of continuous exploration and in-depth research. Since the concept of knowledge representation learning has been proposed, various models have been developed and demonstrated their superior ability in knowledge fusion and reasoning. In this paper, we investigate the progress and function of representation learning models adopted in knowledge fusion and reasoning, and extract their commonalities and characteristics, which is a perspective that has never been developed before, to provide new ideas for scholars and realize the mutual promotion of knowledge fusion and reasoning. This paper comprehensively reviews classic methods and investigates advanced and emerging works, which are published in authoritative journals or conferences. Furthermore, to overcome the limitations of existing methods, we propose an integrated knowledge representation learning and application framework for CPSS and a series of tensor-based knowledge fusion and reasoning models. Finally, four future directions are discussed in this paper, we analyze the challenges faced by them and suggest promising solutions.

Automated summary

This paper investigates the progress and function of representation learning models adopted in knowledge fusion and reasoning, providing new perspectives and ideas for scholars. The paper comprehensively reviews classic methods and investigates advanced and emerging works. Additionally, an integrated knowledge representation learning framework and tensor-based knowledge fusion and reasoning models are proposed.