Reasoning over large-scale knowledge graphs has long been dominated by embedding-based methods, which focus on representing entities and relationships in vector spaces to perform inference tasks. Despite advancements in knowledge graph completion (KGC), challenges such as data sparsity and the lack of interpretability persist. These issues are critical in biomedical areas, where prediction accuracy and explainability directly impact decision-making.

In light of these limitations, this paper proposes a KGC approach that allows it to leverage both globally structured patterns and locally analogical information. Aiming to enhance the reasoning capability and completeness of knowledge graphs, we develop a better joint inference approach based on the rules mining and knowledge graph embedding. Specifically, our approach employs knowledge graph embedding techniques to discover analogically similar triples, which are then used to construct more relevant and accurate inference paths. Subsequently, rule mining is integrated to extract structured knowledge patterns through analogical reasoning.

Experimental results demonstrate that our approach performs better than the traditional Knowledge Graph Embedding Model in some specified KGC tasks, which depend highly on the inner construction of the dataset and its relation types. The multi-hop nature of DRKG aligns well with rule-based approaches, where learned rules generalize over multiple instances, aiding in missing link prediction. Beyond improved performance, our method enhances explainability, facilitating transparent inference and enabling backtracking of key prediction results—an important feature for biomedical and high-stakes AI applications.