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    <abstract>Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models. However, the rule learning-based models suffer from low efficiency and generalization while KGE models lack interpretability. To address these challenges, we propose a novel and effective closed-loop neural-symbolic learning framework EngineKG via incorporating our developed KGE and rule learning modules. KGE module exploits symbolic rules and paths to enhance the semantic association between entities and relations for improving KG embeddings and 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 the relevant baselines on link prediction tasks, demonstrating the superiority of our KG inference model in a neural-symbolic learning fashion. The source code and datasets of this paper are available at https://github.com/ngl567/EngineKG.</abstract>
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%0 Conference Proceedings
%T Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference
%A Niu, Guanglin
%A Li, Bo
%A Zhang, Yongfei
%A Pu, Shiliang
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F niu-etal-2022-perform
%X Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models. However, the rule learning-based models suffer from low efficiency and generalization while KGE models lack interpretability. To address these challenges, we propose a novel and effective closed-loop neural-symbolic learning framework EngineKG via incorporating our developed KGE and rule learning modules. KGE module exploits symbolic rules and paths to enhance the semantic association between entities and relations for improving KG embeddings and 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 the relevant baselines on link prediction tasks, demonstrating the superiority of our KG inference model in a neural-symbolic learning fashion. The source code and datasets of this paper are available at https://github.com/ngl567/EngineKG.
%U https://aclanthology.org/2022.coling-1.119
%P 1391-1400

Markdown (Informal)

[Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference](https://aclanthology.org/2022.coling-1.119) (Niu et al., COLING 2022)

• Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference (Niu et al., COLING 2022)

ACL

• Guanglin Niu, Bo Li, Yongfei Zhang, and Shiliang Pu. 2022. Perform like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1391–1400, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.