Survey on Rule Mining for Knowledge Graph

doi:10.3778/j.issn.1002-8331.2210-0014

Abstract

Abstract: Rule mining for knowledge graph is to extract the implied rule knowledge from the knowledge graph, the rules which have the advantages of high accuracy and strong interpretability can be used to knowledge graph completion, denoising, data interpretion and other issues. The latest research progress of rule mining methods in the field of knowledge graph are reviewed, the rule mining methods based on path traversal, frequent itemset and representation learning are introduced in detail. The characteristics, performance and existing problems of various methods are analyzed. The quality evaluation function of rules are discussed. Finally, the future research direction and prospect of this field are discussed and prospected.

Key words: knowledge graph, rule mining, rule quality evalution, path traversal, representation learning, frequent itemsets

摘要： 面向知识图谱的规则挖掘是从知识图谱中抽取出隐含的规则知识，应用于知识图谱补全、去噪、数据解释等问题，具有准确度高、可解释性强的优势。综述近年来知识图谱领域规则挖掘方法的最新研究进展，详细介绍了基于路径遍历、频繁项集和表示学习的规则挖掘方法，分析各类不同方法的特点、性能和存在问题，同时对规则的质量评估函数进行归纳总结，并探讨和展望了该领域未来的研究方向和前景。

关键词: 知识图谱, 规则挖掘, 规则质量评估, 路径遍历, 表示学习, 频繁项集

LIU Hongbo, CHEN Yue, LU Jicang, HOU Xuemei, YANG Kuiwu. Survey on Rule Mining for Knowledge Graph[J]. Computer Engineering and Applications, 2023, 59(14): 30-38.

刘洪波, 陈越, 卢记仓, 侯雪梅, 杨奎武. 面向知识图谱的规则挖掘研究综述[J]. 计算机工程与应用, 2023, 59(14): 30-38.

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