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Operations Research Transactions >

2025 , Vol. 29 >Issue 1: 41 - 54

DOI: https://doi.org/10.15960/j.cnki.issn.1007-6093.2025.01.004

A new algorithm for improving the completion performance of knowledge graph of long-tail data

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  • 1. School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China

Received date: 2021-12-26

  Online published: 2025-03-08

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, 2025, All rights reserved. Unauthorized reproduction is prohibited.
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Abstract

Knowledge graph is an important semantic data in many intelligent applications, but the incompleteness of its data brings many difficulties to practical applications. Therefore, it is necessary to complete the missing semantic information in the knowledge graph. Knowledge graph embedding is one of the important methods of knowledge graph completion. This type of method usually has better results in the case of non-long-tail data, but its effect is poor in the case of long-tail data. Since the semantics of non-long-tail data is richer, in order to improve the effect of knowledge graph completion in the case of long-tail data, this paper transfers non-long-tail data as supervised knowledge to long-tail data, and proposes a new algorithm——the dual embedding method with the idea of expectation maximization algorithm, to improve the completion performance of knowledge graph, and its practical application effect. Through the comparative experiment of link prediction task in FB15K data set, the experimental results show that the dual embedding method with the idea of expectation maximization algorithm proposed in this paper is effective.

Key words： knowledge graph completion; knowledge graph embedding; expectation maximization algorithm; dual embedding meth

Cite this article

Miaohui HE, Xuxiang DUAN, Zhiyou WU . A new algorithm for improving the completion performance of knowledge graph of long-tail data[J]. Operations Research Transactions, 2025 , 29(1) : 41 -54 . DOI: 10.15960/j.cnki.issn.1007-6093.2025.01.004

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