Knowledge-graph representation learning method based on multiple semantemes

Abstract

The invention discloses a knowledge-graph representation learning method based on multiple semantemes. The method includes the steps that different semantemes of relationships are considered, a modelbased on translation between physical vectors and relationship vectors and different semantemes of relationships in a triple structure are used in cooperation with projected vectors of head-tail entities to define relationship matrixes, correlation between the physical vectors and the relationship vectors in the triple structure is well expressed, and more parameters are not introduced; the physical vectors and the relationship vectors are correlated through loss functions, and the loss functions are optimized; when the optimization target is achieved, all the physical vectors and all the relationship vectors in knowledge graphs can be learned. By means of the knowledge-graph representation learning method based on the multiple semantemes, the problems of the heterogeneity and the unbalancedness between entities and relationships in a knowledge base are solved, and the entities and the relationships and correlation between the entities and the relationships are more accurately expressed, and are applied to large-scale knowledge graphs.

Description

technical field [0001] The invention relates to the technical field of knowledge graphs, in particular to a knowledge graph representation learning method based on multiple semantics. Background technique [0002] With the rapid development of today's society, we have gradually entered an era of information and intelligence. Masses of new data and information are generated every day in different forms. The mobile Internet has become the most effective and convenient information acquisition platform in today's society. Users' demand for real information acquisition is increasingly urgent. How to obtain effective information from massive data has become a major problem in many fields. The knowledge graph came into being from this. [0003] As a new knowledge representation method, knowledge graph belongs to the category of semantic web. Its goal is to describe various entities and concepts in the real world, as well as the association between these entities and concepts, and...

AI Technical Summary

Problems solved by technology

[0005] What the present invention aims to solve is the problem that existing knowledge map representation learning methods cannot accurately represent the relationship between entities under different semantics, and provides a knowledge map representation learning method based on multiple semantics to improve knowledge Spectral Accuracy

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