Knowledge graph embedding method, system and equipment based on tensor decomposition

Abstract

The embodiment of the invention relates to the technical field of knowledge graphs, and discloses a knowledge graph embedding method, system and device based on tensor decomposition. The method comprises the steps of firstly obtaining a tensor corresponding to a current knowledge graph; carrying out Tucker decomposition on the tensor to obtain a tensor after decomposition; determining a fact scorecorresponding to the decomposed tensor; and updating the embedded vector corresponding to the preset knowledge graph embedded model according to the fact score, and performing an embedding operationof the current knowledge graph through the updated embedded vector. Obviously, the embodiment of the invention provides a better embedding mode for the N-element knowledge graph, specifically, the tensor decomposition operation is combined on the knowledge graph embedding operation, and finally, the accuracy of the embedding operation can be improved; meanwhile, the parameter complexity is greatlyreduced, and the operation efficiency is improved. Moreover, the embodiment can perform joint decomposition based on the Tucker and the tensor loop expression.

Description

technical field [0001] The present invention relates to the technical field of knowledge graphs, in particular to a method, system and device for embedding knowledge graphs based on tensor decomposition. Background technique [0002] Knowledge Graph is a data format that structurally represents and stores facts in the real world in the form of a graph, in which the things and concepts contained in the facts correspond to the entities in the knowledge graph, and the relationships between entities correspond to the Edges in the knowledge graph. [0003] At present, the extensively researched knowledge graph sets mostly use binary relationships, that is, facts are described by triples (r, h, t), where h and t represent the head entity and tail entity respectively, and r represents the specific 2-element relation. [0004] However, there are more multivariate relations in the real world, that is, relations with more than 2 elements. For example, an N-element relationship will ...

AI Technical Summary

Problems solved by technology

[0006] However, since the N-ary knowledge map is not complete, there are often a large number of missing facts, and it is necessary to infer the missing relationship between entities based on the existing N-ary knowledge map information, that is, the completion operation of the N-ary knowledge map is required

[0007] As far as the completion operation of the N-ary knowledge graph is concerned, there are many defects in the existing completion methods for the N-ary knowledge graph. The N-ary relationships that appear have strong assumptions, which can only represent a part of the N-ary relationships, and are not suitable for the completion of diverse N-ary knowledge graphs; at the same time, most data operations are limited to 2-element knowledge The dimension of map is difficult to expand to N elements, and the universality is poor, and the inherent attributes and laws in the multivariate relationship cannot be discovered.

Images