提高长尾数据知识图谱补全性能的一种新算法

doi:10.15960/j.cnki.issn.1007-6093.2025.01.004

Abstract

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

CLC Number:

O224
TP181

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.

Figures/Tables 8

References 23

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	$ \alpha _1 $	$ \alpha _2 $	$ T $	$ \gamma $	neg	$ \left\| {{{{\cal L}}^e}} \right\| $	$ \left\| {{{{\cal L}}^r}} \right\| $
TransE	0.3	$ - $	300	3	25	$ - $	$ - $
TransE-DEM	$ - $	0.000 5	300	3	25	300	200
TransH	0.3	$ - $	300	3	25	$ - $	$ - $
TransH-DEM	$ - $	0.000 5	300	3	25	300	200
TransD	0.3	$ - $	300	3	25	$ - $	$ - $
TransD-DEM	$ - $	0.000 5	300	3	25	300	200

冗余关系	反向关系	笛卡尔积关系	近似冗余关系
数量	429 543	983	29 288
百分比/%	88.9	0.2	6.0

	训练集			测试集
	非长尾数据	长尾数据	总数	非长尾数据	长尾数据	总数
数量	438 364	44 778	483 142	53 649	5 422	59 071

	MR	FMR	MRR	FMRR	Hit@10	FHit@10
TransE	224.81	129.40	0.248	0.418	0.498	0.658
TransE-DEM	$\underline{\textbf{201.88}}$	$\underline{\textbf{105.03}}$	$\textbf{0.256}$	$\textbf{0.425}$	$\textbf{0.502}$	$\textbf{0.670}$
TransH	221.45	126.64	0.245	0.413	0.495	0.657
TransH-DEM	$\textbf{218.20}$	$\textbf{121.34}$	$\underline{\textbf{0.259}}$	$\underline{\textbf{0.434}}$	$\underline{\textbf{0.506}}$	$\underline{\textbf{0.675}}$
TransD	221.82	126.62	0.246	0.417	0.497	0.660
TransD-DEM	$\textbf{209.84}$	$\textbf{113.41}$	$\textbf{0.258}$	$\textbf{0.430}$	$\textbf{0.505}$	$\textbf{0.670}$

	MR	FMR	MRR	FMRR	Hit@10	FHit@10
TransE	203.17	119.80	0.250	0.427	0.515	0.674
TransE-DEM	$\underline{\textbf{180.61}}$	$\underline{\textbf{97.79}}$	$\textbf{0.260}$	$\textbf{0.433}$	$\textbf{0.519}$	$\textbf{0.683}$
TransH	200.73	117.56	0.246	0.422	0.511	0.671
TransH-DEM	$\textbf{195.94}$	$\textbf{112.51}$	$\underline{\textbf{0.262}}$	$\underline{\textbf{0.441}}$	$\underline{\textbf{0.523}}$	$\underline{\textbf{0.688}}$
TransD	199.81	116.63	0.249	0.427	0.514	0.676
TransD-DEM	$\textbf{188.55}$	$\textbf{105.68}$	$\textbf{0.260}$	$\textbf{0.436}$	$\textbf{0.522}$	$\textbf{0.682}$

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

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	MR	FMR	MRR	FMRR	Hit@10	FHit@10
TransE	438.86	224.38	0.219	0.328	0.329	0.504
TransE-DEM	$\underline{\textbf{412.31}}$	$\underline{\textbf{176.66}}$	$\textbf{0.219}$	$\textbf{0.349}$	$\textbf{0.334}$	$\textbf{0.538}$
TransH	426.43	216.45	0.226	0.331	0.332	0.510
TransH-DEM	$\textbf{438.51}$	$\textbf{208.68}$	$\textbf{0.229}$	$\textbf{0.368}$	$\underline{\textbf{0.335}}$	$\textbf{0.543}$
TransD	439.65	225.48	0.217	0.326	0.325	0.507
TransD-DEM	$\textbf{420.48}$	$\textbf{189.92}$	$\underline{\textbf{0.234}}$	$\underline{\textbf{0.370}}$	$\textbf{0.334}$	$\underline{\textbf{0.544}}$