Representation method and system of knowledge graph and text information based on referring sentence

Abstract

The invention discloses a representation method and system of a knowledge graph and text information based on a referential sentence, and relates to the technical field of machine learning. The method includes: modeling the knowledge graph to obtain an entity vector and a relationship vector; The deep semantic information related to the relationship is obtained, and the knowledge modeling is carried out to obtain the textualized relationship vector; the deep semantic information related to the entity contained in the plain text is obtained, and the knowledge modeling is carried out to obtain the textualized entity vector; based on the Entity vectors, the relationship vectors, the textualized relationship vectors, the textualized entity vectors, and the word vectors construct optimization parameters to realize the joint representation of knowledge graphs and text information. The present invention uses "referring sentences" to perform textual modeling on entities in the knowledge map, and realizes noise reduction for the joint representation of the knowledge map and text information, thereby improving the quality of knowledge representation and deduction.

Description

technical field [0001] The present invention relates to the technical field of machine learning, and in particular, to a method and system for representing a knowledge graph and text information based on a referential sentence. Background technique [0002] Knowledge Graph (Knowledge Graph) has attracted the attention of many research fields in recent years because of its ability to effectively model and describe abstract concepts and concrete instances in the real world. The knowledge graph representation learning method can effectively alleviate the above problems by mapping the entities and relationships in the knowledge graph to a low-dimensional vector space, and enhance the knowledge learning ability in knowledge representation, knowledge deduction, knowledge fusion, and knowledge completion. More and more studies have found that additional text information can provide rich semantic resources for knowledge graph representation, which plays an important auxiliary role i...

AI Technical Summary

Problems solved by technology

Some models propose a joint representation learning model that maps the entities in the knowledge graph and the words in the text to the same vector space through an alignment mechanism, and another scheme models the contextual information to a certain extent. The above models use the text However, the widespread noise in the text information has greatly weakened the knowledge representation ability of these models. Not all sentences containing an entity are helpful for explaining and modeling the entity.

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