Data processing method and device in graph neural network, equipment and storage medium

Abstract

The invention provides a data processing method and apparatus in a graph neural network, an electronic device and a storage medium. The method comprises the steps of obtaining an initial structure ofa first graph neural network and to-be-processed data in a data stream; triggering a decoupling process and decoupling the relational triples in the first graph neural network into a plurality of embedded components; determining graph embedding corresponding to different embedding components based on the different embedding components matched with the relational triples in the first graph neural network; triggering an updating process to update the relational triples in the initial structure of the first graph neural network to form a second graph neural network; therefore, dynamic data is processed by utilizing the graph neural network, continuous learning of the graph neural network can be achieved in different data to form a new graph neural network, and knowledge acquired by the initial graph neural network is reserved, so that the richness and the perspectiveness of data processing can be improved, and the use experience of a user is improved.

Description

technical field [0001] The present invention relates to information processing technology, in particular to a data processing method, device, equipment and storage medium in a graph neural network. Background technique [0002] Artificial intelligence (AI, Artificial Intelligence) is a comprehensive technology of computer science. By studying the design principles and implementation methods of various intelligent machines, the machines have the functions of perception, reasoning and decision-making. Artificial intelligence technology is a comprehensive subject that involves a wide range of fields, such as natural language processing technology and machine learning / deep learning. It is believed that with the development of technology, artificial intelligence technology will be applied in more fields and play a more important role. increasingly important value. [0003] The graph neural network in related technologies often conducts model training through a fixed training mod...

AI Technical Summary

Problems solved by technology

[0003] The graph neural network in related technologies often conducts model training through a fixed training mode, ignoring the fact that the data in the real world is constantly increasing (data changes in real time), which makes the data processing results of the graph neural network in practical applications often There is a large deviation, which affects the use of users

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