Abstract：Knowledge graph reasoning is an essential approach to address the incompleteness of knowledge graphs. The existing embedding-based reasoning models rely on accurate facts and suffer from poor interpretability. Rule-based reasoning models depend on the completeness of knowledge graphs, resulting in low inference performance on sparse data and an inability to express inference patterns accurately. To address these issues, a model of knowledge graph reasoning combining rule inference patterns and fact embedding(RPFE) is proposed. First, BoxE is employed as the base embedding model to achieve the embedding representation of facts. Second, the inference pattern diversity functions are designed to assist the embedding models in capturing the rules of different inference patterns, providing intuitive embedded interpretation for rule learning. Then, the fact distance consistency scoring functions are proposed to enhance the embedding representation. Finally, the rules and fact scores are optimized to compensate the lack of high-quality facts in knowledge graphs and improve the interpretability of the reasoning. Experiments on three public datasets indicate that the RPFE yields excellent performance in knowledge graph reasoning.
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