A Distributed Framework Construction Method for Knowledge Graph Representation Learning

Abstract

The invention provides a knowledge graph representation learning-oriented distributed framework construction method, which comprises the following steps of: S100, constructing a virtual cluster of a distributed framework by taking a plurality of virtual machines as nodes, and initializing the virtual cluster; s200, constructing a knowledge representation learning model of the knowledge graph on the virtual cluster, and establishing a mapping relationship of the knowledge representation learning model at each node; s300, enabling the node to obtain an input material through network search, input the input material into the knowledge representation learning model for training, and output a knowledge material after training; and S400, combining and processing the knowledge materials output after training to obtain a knowledge graph, and storing the knowledge graph in a database. The distributed framework virtual cluster adopted in the method can provide huge data processing capacity, andknowledge graph representation learning can be efficiently achieved through cooperation of all the nodes.

Description

technical field [0001] The invention relates to the field of computer science and application technology, in particular to a method for constructing a distributed framework for knowledge map representation learning. Background technique [0002] Knowledge graph (Knowledge Graph), known as knowledge domain visualization or knowledge domain mapping map in the library and information industry, is a series of different graphics showing the knowledge development process and structural relationship, using visualization technology to describe knowledge resources and their carriers, mining , analyze, construct, map and display knowledge and their interconnections. The knowledge map is a combination of theories and methods of applied mathematics, graphics, information visualization technology, information science and other disciplines with metrology citation analysis, co-occurrence analysis and other methods, and uses the visual map to vividly display the core structure and developme...

AI Technical Summary

Problems solved by technology

[0005] The amount of data processing for knowledge graph representation learning is huge, and distributed processing is an inevitable choice. However, how to build an efficient distributed framework for data processing of knowledge graph representation learning is a current problem that must be faced

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