Entity alignment method based on heterogeneous graph attention network

An attention, heterogeneous graph technology, applied in the field of knowledge fusion, can solve the problems of huge and complex knowledge base data, insufficient alignment accuracy, and high time complexity of entity alignment algorithms

Active Publication Date: 2021-05-14
南京樯图数据研究院有限公司
View PDF10 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The current entity alignment related technologies and methods are derived from entity matching in the database. However, in practical applications, there are many problems and challenges, the most prominent of which are the problems of computation...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Entity alignment method based on heterogeneous graph attention network
  • Entity alignment method based on heterogeneous graph attention network
  • Entity alignment method based on heterogeneous graph attention network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0049] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention All modifications of the valence form fall within the scope defined by the appended claims of the present application.

[0050] A method for entity alignment based on heterogeneous graph attention networks, such as figure 1 shown, including the following steps:

[0051] Step 1. Based on the word vector obtained from the BERT pre-trained entity name, the entity semantic name vector is calculated according to the word vector, and clustered according to the obtained entity semantic name vector, and the entity is divided into class to get entity class information.

[0052] According to ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an entity alignment method based on a heterogeneous graph attention network, and the method comprises the steps: dividing entities into different categories based on clustering; secondly, learning an embedded vector of an entity based on the heterogeneous graph attention network; calculating the similarity between different entity categories based on entity embedded vectors, and obtaining the similarity of any entity pair by combining the vector similarity; and finally, modeling an entity alignment problem into an integer programming problem, and solving and obtaining an alignment result meeting a one-to-one alignment constraint. The method is not only low in time complexity, but also high in entity alignment precision.

Description

technical field [0001] The invention belongs to the field of knowledge fusion, and in particular relates to an entity alignment method based on a heterogeneous graph attention network related to entity alignment. Background technique [0002] With the expansion of the scale of knowledge bases and the increase of the number of entities, the importance of entity links between different knowledge bases has become increasingly prominent, and entity alignment of multi-knowledge graphs has become a hot research direction. [0003] The current entity alignment-related technologies and methods are derived from entity matching in the database. However, in practical applications, there are many problems and challenges, the most prominent of which are the problems of computational complexity and data alignment quality. Under the condition of big data, the amount of data in the knowledge base is huge and complicated, the traditional entity alignment algorithm has high time complexity, a...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F40/295G06F40/30G06F16/35G06N3/04
CPCG06F40/295G06F40/30G06F16/35G06N3/045
Inventor 王晓杨林瑶程振荣辛柯俊王飞跃
Owner 南京樯图数据研究院有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap