From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs

• URL: http://arxiv.org/abs/2403.05130v1
• Date: Fri, 8 Mar 2024 07:55:42 GMT
• Title: From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs
• Authors: Wangtao Sun, Shizhu He, Jun Zhao, Kang Liu
• Abstract summary: We propose the concept of tree-like rules on knowledge graphs to expand the application scope. We propose an effective framework for refining chain-like rules into tree-like rules.
• Score: 26.237564631208354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With good explanatory power and controllability, rule-based methods play an important role in many tasks such as knowledge reasoning and decision support. However, existing studies primarily focused on learning chain-like rules, which limit their semantic expressions and accurate prediction abilities. As a result, chain-like rules usually fire on the incorrect grounding values, producing inaccurate or even erroneous reasoning results. In this paper, we propose the concept of tree-like rules on knowledge graphs to expand the application scope and improve the reasoning ability of rule-based methods. Meanwhile, we propose an effective framework for refining chain-like rules into tree-like rules. Experimental comparisons on four public datasets show that the proposed framework can easily adapt to other chain-like rule induction methods and the refined tree-like rules consistently achieve better performances than chain-like rules on link prediction. The data and code of this paper can be available at https://anonymous.4open.science/r/tree-rule-E3CD/.

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