MLALogic:Knowledge Reasoning Model Combining Knowledge Representation with Multi-head Latent Attention Mechanism

Knowledge reasoning technology is a method proposed to solve missing problems in knowledge graphs, which has been developing in recent years. In order to solve the problems of low accuracy, poor interpretability, and weak applicability in knowledge reasoning, a knowledge reasoning model called MLALogic is proposed, which combines HousE with multi-head latent attention mechanism. It consists of a rule generator with multi-head latent attention mechanism and a rule predictor with HousE embedding. The rule generator generates the rules required for reasoning and passes them into the predictor, which updates and then obtains scores for different rules, After that, the generator and predictor are continuously trained and optimised by the EM algorithm. Specifically, The model employs a multi-head latent attention mechanism to select more relevant relationships as rules, thereby improving model accuracy and reducing training costs. Additionally, the integration of HousE enhances flexibility in handling complex and multi-relational data.