Knowledge graph representation learning method for integrating text semantic features based on attention mechanism

Abstract

The invention relates to a knowledge graph and discloses a knowledge graph representation learning method for integrating text semantic features based on an attention mechanism. The method solves theproblems that semantic features are insufficient due to the fact that a translation model does not utilize description texts of entities and relations, semantic features cannot be fused into entitiesand relations at the same time by a multi-source information embedding method, and the text extraction effect is poor. The method comprises the steps of firstly obtaining and processing description texts of entities and relationships to obtain text semantic features of the entities and the relationships, then constructing a projection matrix of the entities by utilizing the semantic features of the entities and the relationships, projecting entity vectors into a relationship space, modeling in the relationship space by utilizing a translation thought, and carrying out representation learning,so as to model a many-to-many complex relationship. The method is suitable for representation learning of the knowledge graph.

Description

technical field [0001] The invention relates to a knowledge map, in particular to a knowledge map representation learning method based on an attention mechanism and incorporating text semantic features. Background technique [0002] With the development of Internet technology, data shows explosive growth. However, due to the multi-source and heterogeneous content on the Internet and the loose organizational structure, it is difficult to efficiently use the information in it. Therefore, Google proposed the concept of Knowledge Graph in May 2012, aiming to integrate massive unstructured or semi-structured Data is transformed into standardized, unified, reliable and effective structured knowledge, thus forming a highly interconnected semantic network to provide support for data mining and intelligent services. [0003] The knowledge graph can be regarded as a network with a directed graph structure, in which the nodes of the graph represent entities or concepts, and the edges ...

AI Technical Summary

Problems solved by technology

[0024] The technical problem to be solved by the present invention is to propose a knowledge map representation learning method based on the attention mechanism integrated into the semantic features of the text, to solve the lack of semantic features and multi-source information embedding caused by the failure of the translation model to use the description text of entities and relationships The method fails to incorporate semantic features for entities and relationships at the same time, and the text extraction effect is poor

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