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Entity alignment method for knowledge graphs

A knowledge graph and entity pair technology, applied in the field of knowledge graph data processing, can solve problems such as unsatisfactory performance, achieve the effect of improving alignment accuracy and optimizing the overall alignment effect

Active Publication Date: 2020-04-03
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

Therefore, current entity alignment methods based on structural information do not perform satisfactorily on real-world datasets.

Method used

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  • Entity alignment method for knowledge graphs
  • Entity alignment method for knowledge graphs
  • Entity alignment method for knowledge graphs

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Embodiment Construction

[0030] The present invention will be further described below in conjunction with the accompanying drawings, but the present invention is not limited in any way. Any transformation or replacement based on the teaching of the present invention belongs to the protection scope of the present invention.

[0031] Such as figure 1 As shown, an entity alignment method for knowledge graph fusion includes the following steps:

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

[0033] Step 2, use the graph convolutional network to learn the structure vector of the entity; represent the name of the entity as a word vector;

[0034] Step 3, calculate the structural distance and word feature distance of the entity;

[0035] Step 4, and combine the two distances into a comprehensive distance to represent the similarity of entities;

[0036] Step 5, perform entity recognition and alignment according to the calculation results of the similarity degree, and obtain similar entity pair...

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Abstract

The invention discloses an entity alignment method for knowledge graphs. The entity alignment method comprises the following steps: acquiring data of two knowledge graphs; learning the structure vector of the entity by using a graph convolution network, and expressing the name of the entity as a word vector; calculating a structural distance and a word feature distance of the entity; fusing the two distances with a comprehensive distance to represent the similarity degree of the entity; and performing entity identification alignment according to a calculation result of the similarity degree. According to the method, an entity alignment basic framework fusing structural features and entity name features is designed; and the preorder alignment result is reordered by adopting a word shift distance model, so that entity name information is fully mined, and the accuracy and timeliness of entity alignment are improved.

Description

technical field [0001] The invention belongs to the field of knowledge graph data processing, and in particular relates to an entity alignment method for knowledge graphs. Background technique [0002] In recent years, a large number of knowledge graphs (knowledge graph, KG) have emerged, such as YAGO, DBpedia, NELL, and Chinese CN-DBpedia, Zhishi.me, etc. These large-scale knowledge graphs play an important role in intelligent services such as question answering systems and personalized recommendations. In addition, in order to meet the needs of specific domains, more and more domain knowledge graphs, such as medical knowledge graphs, have been derived. 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 oth...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06F40/295G06N3/04G06N3/08
CPCG06F16/367G06N3/08G06N3/045
Inventor 赵翔曾维新唐九阳徐浩谭真殷风景葛斌肖卫东
Owner NAT UNIV OF DEFENSE TECH
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