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Rule Learning over Knowledge Graphs: A Review

Authors Hong Wu , Zhe Wang , Kewen Wang , Pouya Ghiasnezhad Omran , Jiangmeng Li

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Author Details

Hong Wu
  • School of Information and Communication Technology, Griffith University, Australia
Zhe Wang
  • School of Information and Communication Technology, Griffith University, Australia
Kewen Wang
  • School of Information and Communication Technology, Griffith University, Australia
Pouya Ghiasnezhad Omran
  • School of Computing, The Australian National University, Australia
Jiangmeng Li
  • Institute of Software, Chinese Academy of Sciences, China

Acknowledgements

The authors would like to thank the editors and the anonymous referees for their constructive comments that have helped improve the quality of this paper.

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Hong Wu, Zhe Wang, Kewen Wang, Pouya Ghiasnezhad Omran, and Jiangmeng Li. Rule Learning over Knowledge Graphs: A Review. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 7:1-7:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/TGDK.1.1.7

Abstract

Compared to black-box neural networks, logic rules express explicit knowledge, can provide human-understandable explanations for reasoning processes, and have found their wide application in knowledge graphs and other downstream tasks. As extracting rules manually from large knowledge graphs is labour-intensive and often infeasible, automated rule learning has recently attracted significant interest, and a number of approaches to rule learning for knowledge graphs have been proposed. This survey aims to provide a review of approaches and a classification of state-of-the-art systems for learning first-order logic rules over knowledge graphs. A comparative analysis of various approaches to rule learning is conducted based on rule language biases, underlying methods, and evaluation metrics. The approaches we consider include inductive logic programming (ILP)-based, statistical path generalisation, and neuro-symbolic methods. Moreover, we highlight important and promising application scenarios of rule learning, such as rule-based knowledge graph completion, fact checking, and applications in other research areas.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Knowledge representation and reasoning
  • Information systems → Data mining
Keywords
  • Rule learning
  • Knowledge graphs
  • Link prediction

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