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

arXiv: https://arxiv.org/abs/2403.05130 · HTML: https://mlsanity.com/paper/2403.05130/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [Neural Logic Machines](https://mlsanity.com/paper/1904.11694/) (2019)
- [RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2010.04029/) (2020)
- [Learn to Explain Efficiently via Neural Logic Inductive Learning](https://mlsanity.com/paper/1910.02481/) (2019)
- [Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion](https://mlsanity.com/paper/2004.04412/) (2020)
- [Differentiable Logic Machines](https://mlsanity.com/paper/2102.11529/) (2021)
- [ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion](https://mlsanity.com/paper/2212.03091/) (2022)

## Built on by (most cited first, 1 of 1)

- [When No Paths Lead to Rome: Benchmarking Systematic Neural Relational Reasoning](https://mlsanity.com/paper/2510.23532/) (2025)

## Abstract (arXiv)

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/.