Knowledge graph reasoning completion method and system based on reinforcement learning

Abstract

The invention discloses a knowledge graph reasoning completion method and system based on reinforcement learning, and the method comprises the steps: loading data, analyzing the data, and obtaining the vector representation of entities and relationships of a knowledge graph; embedding the entities and the relationships on the basis of a TransE model, and pre-training a pre-constructed strategy network to obtain a pre-trained strategy network; retraining the pre-trained strategy network through a plurality of reward functions to obtain a trained strategy network; and inputting the knowledge graph to be tested into the trained strategy network to complete the completion of the knowledge graph. The system comprises an analysis module, a pre-training module, a re-training module and a completion module. Through the design of the reward function, the complemented relation reasoning path is more reliable and has diversity. The knowledge graph reasoning completion method and system based on reinforcement learning can be widely applied to the field of knowledge graphs.

Description

technical field [0001] The invention belongs to the field of knowledge graphs, in particular to a reinforcement learning-based reasoning and completion method and system for knowledge graphs. Background technique [0002] Knowledge graph is a large-scale semantic network, including entities, concepts and various semantic relationships among them. Among them, the method of reinforcement learning for knowledge map reasoning is based on path reasoning. The method of reinforcement learning is used to infer the path between two entities, that is, the relationship set contained between entities, so as to find a reliable prediction path between entity pairs. , to realize the completion of the knowledge graph, but the current knowledge graph representation learning model ignores this aspect and lacks consideration of the diversity of relationships between entities in the knowledge graph. Contents of the invention [0003] In order to solve the above-mentioned technical problems, ...

AI Technical Summary

Problems solved by technology

Among them, the method of reinforcement learning for knowledge map reasoning is based on path reasoning. The method of reinforcement learning is used to infer the path between two entities, that is, the relationship set contained between entities, so as to find a reliable prediction path between entity pairs. , to realize the completion of the knowledge graph, but the current knowledge graph representation learning model ignores this aspect and lacks consideration of the diversity of relationships between entities in the knowledge graph

Images