EngineKGI: Closed-Loop Knowledge Graph Inference

Knowledge Graph (KG) inference is the vital technique to address the natural incompleteness of KGs. The existing KG inference approaches can be classified into rule learning-based and KG embedding-based models. However, these approaches cannot well balance accuracy, generalization, interpretability and efficiency, simultaneously. Besides, these models always rely on pure triples and neglect additional information. Therefore, both KG embedding (KGE) and rule learning KG inference approaches face challenges due to the sparse entities and the limited semantics. We propose a novel and effective closed-loop KG inference framework EngineKGI operating similarly as an engine based on these observations. EngineKGI combines KGE and rule learning to complement each other in a closed-loop pattern while taking advantage of semantics in paths and concepts. KGE module exploits paths to enhance the semantic association between entities and introduces rules for interpretability. A novel rule pruning mechanism is proposed in the rule learning module by leveraging paths as initial candidate rules and employing KG embeddings together with concepts for extracting more high-quality rules. Experimental results on four real-world datasets show that our model outperforms other baselines on link prediction tasks, demonstrating the effectiveness and superiority of our model on KG inference in a joint logic and data-driven fashion with a closed-loop mechanism.