Knowledge graph entity semantic space embedding method based on graph second-order similarity

Abstract

The invention discloses a knowledge graph entity semantic space embedding method based on graph second-order similarity, and the method comprises the steps: (1) inputting a knowledge graph data set and a maximum number of iterations; (2) calculating first-order and second-order similarity vector representations through first-order and second-order similarity feature embedding processing by considering a relation between entities through a graph attention mechanism to obtain first-order and second-order similarity semantic space embedding representations; (3) carrying out weighted summation onthe final first-order similarity vector and the final second-order similarity vector of the entity to obtain a final vector representation of the entity, inputting a translation model to calculate a loss value to obtain a graph attention network and a graph neural network residual, and iterating the network model; And (4) performing link prediction and classification test on the network model. According to the method, the relation between entities is mined by using a graph attention mechanism for the first time, and patents have a relatively good effect in the application fields of link prediction, classification and the like of the knowledge graph.

Description

technical field [0001] The present invention belongs to the technical field of knowledge map, and more specifically, relates to a Figure II A semantic space embedding method for knowledge graph entities with first-order similarity. Background technique [0002] With the rapid development of Internet technology, a large amount of data is generated every day, how to extract and utilize valuable information from massive data has become a challenging problem, so Google proposed the concept of knowledge graph. The essence of the knowledge graph is a directed graph, which consists of a triplet consisting of a head entity, a relationship, and a tail entity, such as (Beijing, is...the capital, China); entities are points in the knowledge graph , the relationship is the directed edge in the knowledge graph, and they together form the directed graph structure of the knowledge graph. The knowledge graph records the relationship between entities and entities, realizes the structured r...

AI Technical Summary

Problems solved by technology

[0008](3) Since most of the current knowledge map entity representation learning methods only consider the first-order similarity features of the graph structure, link prediction and classification involving neighbor feature extraction The effect of feature experiment is not ideal

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