Unsupervised knowledge graph entity alignment method and equipment

A technology of knowledge graphs and entity pairs, applied in the field of knowledge graphs in natural language processing, can solve problems such as relying on labeled data, entities without corresponding matching, and entity alignment methods without matching, so as to achieve good knowledge graph embedding and accurate The effect of matching and alignment

Pending Publication Date: 2021-06-11
NAT UNIV OF DEFENSE TECH
View PDF0 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] However, these methods have certain defects: 1) rely on labeled data
However, in real environments, there are always entities

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
  • Unsupervised knowledge graph entity alignment method and equipment
  • Unsupervised knowledge graph entity alignment method and equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0031] figure 1 A schematic flowchart of Embodiment 1 of the present invention is shown. An unsupervised method for aligning knowledge graph entities, including the following steps:

[0032] Step 1, obtain the data of two knowledge graphs;

[0033] Step 2, using the auxiliary information of entities in the knowledge map to generate a text distance matrix;

[0034] Step 3, using the threshold bidirectional nearest neighbor search to generate an initial alignment result as a set of seed entity pairs;

[0035] Step 4, based on the set of seed entity pairs as labeled data, use the graph convolutional network to learn the structural distance matrix of the entity;

[0036] Step 5, fusing the text distance matrix and the structure distance matrix of the entity to obtain the fusion distance matrix;

[0037] Step 6. Carry out progressive learning, adaptively adjust the threshold, use the threshold bidirectional nearest neighbor search and the fusion distance matrix to obtain a newl...

Embodiment 2

[0055] An unsupervised knowledge map entity alignment device, including:

[0056] processor;

[0057] And, a memory for storing executable instructions of the processor;

[0058] Wherein, the processor is configured to execute the entity alignment method in Embodiment 1 by executing the executable instruction.

[0059] Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

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 unsupervised knowledge graph entity alignment method and equipment. The method comprises the following steps: acquiring data of two knowledge graphs; generating a text distance matrix by using the auxiliary information of the entities in the knowledge graph; generating an initial alignment result as a seed entity pair set by using threshold bidirectional nearest neighbor search; on the basis that the seed entity pair set is marked data, learning a structure distance matrix of the entity by using a graph convolutional network; fusing the text distance matrix and the structure distance matrix of the entity to obtain a fused distance matrix; performing progressive learning to obtain a newly generated alignment entity pair, merging the newly generated alignment entity pair into a seed entity pair set, and using the merged seed entity pair set to iteratively update structure embedding; and repeating the first three steps until the number of the newly generated alignment entity pairs is lower than a preset value, and obtaining a final entity alignment result.

Description

technical field [0001] The present invention relates to the technical field of knowledge graph in natural language processing, in particular to an unsupervised method and device for aligning knowledge graph entities. Background technique [0002] In recent years, a large number of knowledge graphs (KG) have emerged, such as YAGO, DBpedia, NELL, and Chinese CN-DBpedia, Zhishi.me, etc. Knowledge graphs have been applied in various fields such as natural language processing and information retrieval. In the process of building a knowledge graph, it is inevitable to make a trade-off between coverage and accuracy. However, any knowledge map cannot be complete or completely correct. [0003] In order to improve the coverage and accuracy of the knowledge graph, a feasible method is to introduce relevant knowledge from other knowledge graphs, because there is redundancy and complementarity of knowledge between the knowledge graphs constructed in different ways. To integrate the k...

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): G06F16/36G06F16/383G06F40/194G06F40/30G06N3/08
CPCG06N3/088G06F16/367G06F16/383G06F40/194G06F40/30
Inventor 赵翔曾维新唐九阳李欣奕谭真谭跃进姜江
Owner NAT UNIV OF DEFENSE TECH
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