From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913635366862848 |
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| author | Sun, Wangtao He, Shizhu Zhao, Jun Liu, Kang |
| author_facet | Sun, Wangtao He, Shizhu Zhao, Jun Liu, Kang |
| contents | 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/. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_05130 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs Sun, Wangtao He, Shizhu Zhao, Jun Liu, Kang Artificial Intelligence 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/. |
| title | From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2403.05130 |