Adaptive knowledge graph representation learning method for integrating graph structure with text information

Abstract

The present invention discloses an adaptive knowledge graph representation learning method for integrating a graph structure with text information, including: (1) sampling a neighbor triple of each of a head entity and a tail entity in a target triple; (2) calculating semantic representations of the target triple, and neighbor triples of its head and tail entities; (3) calculating structure representations of the head and tail entities of the target triple; (4) splicing the semantic representation of the target triple with the structure representations of its head and tail entities, inputting a spliced result into an adaptive classification layer, and calculating a classification result and a classification loss; and (5) optimizing the foregoing module based on an optimization algorithm of gradient descent, until a loss value converges, to obtain a final spliced result between the semantic representation of the target triple and the structure representations of its head and tail entities. The present invention can simultaneously capture the semantic representation and graph structure representation of the knowledge graph, and adaptively integrate and fully utilize the semantic and structure information, thereby imparting better robustness to a knowledge graph with insufficient information.