Method and device for training graph neural network model for characterizing knowledge graph

Abstract

The embodiment of the invention provides a method and a device for training a graph neural network model used for characterizing a knowledge graph, and the method comprises the steps: obtaining a triad from the knowledge graph, and the triad comprises a first node, a second node, and a first connection edge pointing to the second node from the first node; then, in an edge embedding layer, determining a corresponding first edge vector according to the relationship type corresponding to the first connecting edge and the edge attribute characteristics; in the node embedding layer, the first nodeand the second node serving as target nodes respectively, conducting multi-level vector embedding according to the node attribute characteristics of the target nodes and a neighbor node set of the target nodes, and therefore a first high-order vector and a second high-order vector corresponding to the first node and the second node are obtained respectively; then, according to the first high-ordervector, the second high-order vector and the first edge vector, determining the probability that the first node is connected to the second node through the first connecting edge, and updating the edge embedding layer and the node embedding layer with the maximization probability as the target;

Description

technical field [0001] One or more embodiments of this specification relate to the field of machine learning, and in particular to a method and device for training a graph neural network model for representing knowledge graphs. Background technique [0002] With the development of the Internet, the content of network data presents an explosive growth trend. Due to the characteristics of large-scale, heterogeneous and diverse, and loose organizational structure of Internet content, it poses challenges for people to effectively obtain information and knowledge. Knowledge Graph, with its powerful semantic processing capabilities and open organization capabilities, has laid the foundation for knowledge-based organizations and artificial intelligence applications. [0003] Knowledge graphs are mainly used to describe various entities and concepts that exist in the real world, as well as the relationship between them, and have strong data description capabilities. Its original i...

AI Technical Summary

Problems solved by technology

However, the learning and representation of knowledge graphs in conventional technologies is not deep and comprehensive enough, which makes their expression ability limited.

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