A Knowledge Graph Representation Learning Method Based on Cosine Metric Rule

Abstract

The invention discloses a learning method for knowledge graph representation based on cosine metric rules. Firstly, entities and relationships in the knowledge graph are randomly embedded into two vector spaces; secondly, by using the statistical rules of candidate entities, the triplet sets and Candidate entity vector set; again use the cosine similarity to construct the scoring function of the target vector and the candidate entity, and evaluate the candidate entity; finally, use the loss function to uniformly train all the candidate entity vectors and target vectors related to each other, and use stochastic gradient descent The algorithm minimizes a loss function. When the optimization goal is achieved, the best representation of each entity vector and relationship vector in the knowledge graph can be obtained, so as to better represent the connection between the entity and the relationship, and can be well applied to large-scale knowledge graph complementation. All of them.

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 cosine metric rules. Background technique [0002] With the advent of the era of big data, the dividend of data has made artificial intelligence technology develop at an unprecedented speed. Great progress has been made in related fields such as knowledge engineering represented by knowledge graph and machine learning represented by representation learning. On the one hand, as the dividends of big data are exhausted by representation learning, the effect of representation learning models tends to become a bottleneck. On the other hand, with the continuous emergence of a large number of knowledge graphs, these treasures of human prior knowledge have not been effectively utilized by representation learning. The fusion of knowledge graph and representation learning has become one of the important ideas to further i...

AI Technical Summary

Problems solved by technology

[0006] What the present invention aims to solve is the problem that the existing knowledge map representation learning method cannot handle the complex relationship of 1-N, N-1, N-N well, and provides a knowledge map representation learning method based on the cosine metric rule

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