# Rule-Guided Compositional Representation Learning on Knowledge Graphs

arXiv: https://arxiv.org/abs/1911.08935 · HTML: https://mlsanity.com/paper/1911.08935/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 16 of 16)

- [Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective](https://mlsanity.com/paper/2202.07412/) (2022)
- [Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction](https://mlsanity.com/paper/2302.02601/) (2023)
- [Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference](https://mlsanity.com/paper/2112.01040/) (2021)
- [Relational Reasoning Networks](https://mlsanity.com/paper/2106.00393/) (2021)
- [Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion](https://mlsanity.com/paper/2306.17034/) (2023)
- [A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning](https://mlsanity.com/paper/2308.07889/) (2023)
- [Replacing Paths with Connection-Biased Attention for Knowledge Graph Completion](https://mlsanity.com/paper/2410.00876/) (2024)
- [Knowledge Graph Embeddings: A Comprehensive Survey on Capturing Relation Properties](https://mlsanity.com/paper/2410.14733/) (2024)
- [Neural Theorem Provers Delineating Search Area Using RNN](https://mlsanity.com/paper/2203.06985/) (2022)
- [IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling](https://mlsanity.com/paper/2301.02445/) (2023)
- [NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning](https://mlsanity.com/paper/2312.09219/) (2023)
- [RNNCTPs: A Neural Symbolic Reasoning Method Using Dynamic Knowledge Partitioning Technology](https://mlsanity.com/paper/2204.08810/) (2022)
- [A Pluggable Common Sense-Enhanced Framework for Knowledge Graph Completion](https://mlsanity.com/paper/2410.04488/) (2024)
- [PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings](https://mlsanity.com/paper/2501.19095/) (2025)
- [MPRM: A Markov Path-based Rule Miner for Efficient and Interpretable Knowledge Graph Reasoning](https://mlsanity.com/paper/2505.12329/) (2025)
- [StruProKGR: A Structural and Probabilistic Framework for Sparse Knowledge Graph Reasoning](https://mlsanity.com/paper/2512.12613/) (2025)

## Abstract (arXiv)

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Besides, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.