Knowledge Reasoning Model Combining HousE with Attention Mechanism
Knowledge reasoning technology is a method proposed to solve the problem of missing knowledge graphs and has been continuously developed in recent years.In order to solve the problems of low accuracy,poor interpretability,and weak applicabili-ty in knowledge reasoning,a knowledge reasoning model called Att-HousE,which combines HousE with Attention Mechanism,is proposed.It consists of a rule generator with attention mechanism and a rule predictor with HousE.The rule generator generates the rules required for reasoning and passes them into the predictor,which updates and then obtains scores for different rules.Af-ter that,the generator and predictor are continuously trained and optimized by the EM algorithm.Specifically,the model is based on RNNLogic and has been improved.The attention mechanism can select more noteworthy relationships as rules,improving the accuracy of the model.HousE has more flexibility in handling complex relationships and is suitable for establishing multilateral relationships.According to experimental results on public datasets,it indicates that Att-HousE's MRR is 6.3％higher than that of RNNLogic when doing reasoning tasks on FB15K-237.For the sparse dataset WN18RR,the Hits@10 of Att-HousE is 2.7％higher than that of RNNLogic.It is demonstrated that the introduction of HousE and attention mechanism can more comprehen-sively grasp and form multilateral relationships,which can improve the accuracy of knowledge reasoning.
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