Logical Rule-Based Knowledge Graph Reasoning: A Comprehensive Survey

With its powerful expressive capability and intuitive presentation, the knowledge graph has emerged as one of the primary forms of knowledge representation and management. However, the presence of biases in our cognitive and construction processes often leads to varying degrees of incompleteness and...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-11, Vol.11 (21), p.4486
Main Authors: Zeng, Zefan, Cheng, Qing, Si, Yuehang
Format: Article
Language:English
Subjects:
Artificial intelligence
Big Data
Classification
Decision making
embedding
Graphs
inductive logic programming
knowledge graph reasoning
Knowledge representation
Logic programming
logical rules
neural network
Neural networks
probabilistic graph
Reasoning
Recommender systems
Semantics
Surveys
Citations: Items that this one cites
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Summary:With its powerful expressive capability and intuitive presentation, the knowledge graph has emerged as one of the primary forms of knowledge representation and management. However, the presence of biases in our cognitive and construction processes often leads to varying degrees of incompleteness and errors within knowledge graphs. To address this, reasoning becomes essential for supplementing and rectifying these shortcomings. Logical rule-based knowledge graph reasoning methods excel at performing inference by uncovering underlying logical rules, showcasing remarkable generalization ability and interpretability. Moreover, the flexibility of logical rules allows for seamless integration with diverse neural network models, thereby offering promising prospects for research and application. Despite the growing number of logical rule-based knowledge graph reasoning methods, a systematic classification and analysis of these approaches is lacking. In this review, we delve into the relevant research on logical rule-based knowledge graph reasoning, classifying them into four categories: methods based on inductive logic programming (ILP), methods that unify probabilistic graphical models and logical rules, methods that unify embedding techniques and logical rules, and methods that jointly use neural networks (NNs) and logical rules. We introduce and analyze the core concepts and key techniques, as well as the advantages and disadvantages associated with these methods, while also providing a comparative evaluation of their performance. Furthermore, we summarize the main problems and challenges, and offer insights into potential directions for future research.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11214486