A knowledge representation learning method based on graph representation learning

Abstract

The invention discloses a knowledge representation learning method based on map representation learning, which comprises the following steps: S1, acquiring a standard graph based on knowledge graph triples and predicates; S2, obtaining vector representations of knowledge graph entities and relationships according to the standard graph; S3. Taking the label of the deep learning classification task as the target entity, according to the vector representation of the knowledge map entity and relationship, and calculating the similarity between the target entities based on the similarity measure, the graph association matrix of the target entity is obtained. This method combines the information contained in the relationship between entities and incorporates inference rules, so it accommodates a large amount of associated information and makes the learned representation better.

Description

technical field [0001] The invention relates to the field of knowledge map representation learning, in particular to a knowledge representation learning method based on map representation learning. Background technique [0002] Most of the traditional knowledge graph representation learning methods are based on translation models. For example, the TransE model regards the relationship in each triple instance as a translation from the head entity to the tail entity, and models entities and relationships through mathematical formal constraints. , and map them to the same vector space. This type of method focuses on the translation process between entities and entities through relationships. The learned representations mainly retain the connections between entities that have direct relationships, and there is no direct relationship. The semantic association information between entities is seriously lost. There are many follow-up improvements based on this, such as mapping enti...

AI Technical Summary

Problems solved by technology

[0003] In view of the above-mentioned shortcomings in the prior art, a knowledge representation learning method based on map representation learning provided by the present invention solves the problem of poor quality of existing knowledge map representation learning methods

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