From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs
<|reference_start|>From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs: 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/.<|reference_end|>
@article{sun2024from,
    title={From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on
  Knowledge Graphs},
    author={Wangtao Sun, Shizhu He, Jun Zhao, Kang Liu},
    journal={arXiv preprint arXiv:2403.05130},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05130},
    primaryClass={cs.AI}
}