From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs
This work addresses the problem of inaccurate reasoning in rule-based methods for knowledge graph tasks, offering an incremental improvement over existing approaches.
The paper tackles the limitation of chain-like rules in knowledge graphs by introducing tree-like rules to improve reasoning accuracy, showing consistent performance gains in link prediction across four datasets.
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/.