Summary of From Chain to Tree: Refining Chain-like Rules Into Tree-like Rules on Knowledge Graphs, by Wangtao Sun et al.
From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs
by Wangtao Sun, Shizhu He, Jun Zhao, Kang Liu
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning method that leverages tree-like rules on knowledge graphs is proposed to improve the reasoning ability of rule-based methods. The existing chain-like rule-based methods have limitations in their semantic expressions and accurate prediction abilities, leading to inaccurate or erroneous results. To address this issue, an effective framework for refining chain-like rules into tree-like rules is introduced. Experimental comparisons on four public datasets demonstrate that the proposed framework can adapt to other chain-like rule induction methods and consistently achieves better performances than chain-like rules on link prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Rule-based methods are used in many tasks such as knowledge reasoning and decision support, but they have limitations. The current method of learning chain-like rules is not good enough because it’s hard for them to express complex ideas or make accurate predictions. To fix this problem, a new way of making tree-like rules on knowledge graphs is proposed. This approach can improve the accuracy of rule-based methods and help them make better decisions. |
Keywords
» Artificial intelligence » Machine learning
