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Knowledge graph (KG) inference aims to address the natural incompleteness ofKGs, including rule learning-based and KG embedding (KGE) models. However, therule learning-based models suffer from low efficiency and generalization whileKGE models lack interpretability. To address these challenges, we propose anovel and effective closed-loop neural-symbolic learning framework EngineKG viaincorporating our developed KGE and rule learning modules. KGE module exploitssymbolic rules and paths to enhance the semantic association between entitiesand relations for improving KG embeddings and interpretability. A novel rulepruning mechanism is proposed in the rule learning module by leveraging pathsas initial candidate rules and employing KG embeddings together with conceptsfor extracting more high-quality rules. Experimental results on four real-worlddatasets show that our model outperforms the relevant baselines on linkprediction tasks, demonstrating the superiority of our KG inference model in aneural-symbolic learning fashion.

Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference