A Survey on Research of Rule-Based Knowledge Graph Reasoning

Subjects: Computer Science >> Other Disciplines of Computer Science submitted time 2023-05-22

ZENG，Zefan CHENG,Qing SI, Yuehang LIU,Zhong

Abstract： With its excellent expressive power and explicit presentation, Knowledge Graph(KG) has become one of the main forms of knowledge representation and management. However, due to biases in the cognitive and construction processes, their are varying degrees of missing and incorrect problems in KGs. Hence, Knowledge Graph Reasoning(KGR) is needed for completion and denoising of KGs. Rule-based KGR methods reason by mining the underlying logic rules, which empowers them strong generalization ability and interpretability. Rules can also be flexibly combined with Neural Networks. Currently, rule-based knowledge graph reasoning methods are constantly emerging, but there is still a lack of systematic categorization and analysis. This study collects the research works related to rule-based knowledge graph reasoning, summarizes four categories of methods, including Inductive Logic Programming based methods, methods combining rules with probability graph,methods combining rules with embedding representation, methods combining rules with Neural Networks. Core ideas, key technologies, advantages and disadvantages of relevant classical methods are then elaborated. Besides, performance of the methods are compared. Finally, current problems and challenges are summarized, and the future development direction is prospected.

Peer Review Status:Awaiting Review

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