Industrial graph fusion method based on graph convolutional neural network

Abstract

The invention discloses an industrial graph fusion method based on a graph convolutional neural network. The method is based on a plurality of constructed industrial sub-graphs, and the method comprises the following steps: constructing a local entity sub-graph of a graph; converting the structure embedding of the entity into the same vector space by using the attribute embedding of the attributetriple in the graph, forming an entity embedding vector, converting an entity alignment problem into a graph matching problem, and further forming a local matching vector by using a graph attention method; propagating local matching information in a graph through GCN to form a graph-level matching vector, and finally obtaining entity alignment in the graph through a double-layer feedforward neuralnetwork. According to the invention, the structural embedding of entities is converted into the same vector space through attribute embedding, the problem that pre-aligned entities are insufficient is solved, and the entity alignment problem in the graph is further converted into the graph matching problem through graph attention. Intelligence support is provided for optimizing the industrial structure, optimizing the regional structure and improving the industrial core competitiveness.

Description

technical field [0001] The invention belongs to the technical field of deep learning and natural language processing, and relates to an industrial map fusion method based on a graph convolutional neural network, which provides decision support for the macroeconomic field. Background technique [0002] In recent years, artificial intelligence has flourished globally, and its success is inseparable from the contribution of knowledge graphs. Constructing a specific field knowledge map represented by the automobile industry in the industrial field provides intellectual support for promoting the new industrialization process in Hubei, optimizing the industrial structure, optimizing the regional structure, enhancing the core competitiveness of the industry, and serving the high-quality industrial development of the province. The patent of the present invention mainly uses the entity alignment technology to effectively integrate the new energy vehicle industry map, the fuel vehicle...

AI Technical Summary

Problems solved by technology

In the current technical implementation, most of the attention is paid to the relational triples in the knowledge graph, taking into account its local structure information, but few works consider the information of the attribute triplets

Images