EngineKGI: Closed-Loop Knowledge Graph Inference
- URL: http://arxiv.org/abs/2112.01040v1
- Date: Thu, 2 Dec 2021 08:02:59 GMT
- Title: EngineKGI: Closed-Loop Knowledge Graph Inference
- Authors: Guanglin Niu, Bo Li, Yongfei Zhang, Shiliang Pu
- Abstract summary: EngineKGI is a novel closed-loop KG inference framework.
It combines KGE and rule learning to complement each other in a closed-loop pattern.
Our model outperforms other baselines on link prediction tasks.
- Score: 37.15381932994768
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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