基于知识图谱嵌入的定义域值域约束补全方法

doi:10.13229/j.cnki.jdxbgxb20200755

Abstract

Abstract:

In this paper， we focus on completing the missing domain and range constraints in knowledge graphs and try to predict missing constraints by knowledge graph embedding models. Considering the structure of constraints completion problem， we introduce two efficient approaches， DCaT-T and DCaT-R， which are derived from translation-based knowledge graph embedding models TransE and RotatE. In particular both DCaT-T and DCaT-R exploit a two-stage training approach to improve the performance of the constraints predicting models. Experimental results show that both DCaT-T and DCaT-R are efficient than entity typing approach SDType， DCaT-T performs better than TransE-based entity typing model ConnectE and the two-stage learning approach can improve the performance of the models further.

Key words: artificial intelligence, knowledge graph, domain and range, constraints completion, knowledge graph embedding

CLC Number:

TP391

Jing-pei LEI,Dan-tong OUYANG,Li-ming ZHANG. Relation domain and range completion method based on knowledge graph embedding[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 154-161.

Figures/Tables 6

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References 18

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数据集	#Entities	#Rel	#Train	#Valid	#Test
FB15k	14 951	1345	483 142	50 000	59 071
WN18	40 943	18	141 442	5000	5000
FB15k?237	14 541	237	272 115	17 535	20 466
WN18RR	40 559	11	86 835	3034	3134

数据集	#C_g（#Dom）	#Train _g	#Valid_g	#Test_g
FB15k	2690	273 710	16 052	19 061
WN18	36	171 254	9006	9028
FB15k?237	474	149 689	5707	6736
WN18RR	22	103 509	2741	2811

Data set	metric	SDType	DCaT-T	DCaT-R
FB15k	MR	86.42	3.77	3.95
	MRR	0.460	0.825	0.815
	Hits@1	0.366	0.761	0.746
	Hits@3	0.496	0.869	0.863
	Hits@10	0.696	0.946	0.944
WN18	MR	1.61	1.15	1.19
	MRR	0.936	0.976	0.968
	Hits@1	0.912	0.965	0.955
	Hits@3	0.968	0.983	0.977
	Hits@10	0.993	0.996	0.996
FB15k?237	MR	10.7	5.340	5.52
	MRR	0.502	0.543	0.545
	Hits@1	0.365	0.365	0.375
	Hits@3	0.567	0.659	0.649
	Hits@10	0.792	0.892	0.879
WN18RR	MR	2.60	2.00	1.70
	MRR	0.771	0.802	0.828
	Hits@1	0.702	0.716	0.729
	Hits@3	0.784	0.860	0.915
	Hits@10	0.901	0.990	0.993

metric	ConnectE	DCaT?T	DCaT?T （N）	DCaT?T （MIX）
MR	14.65	3.770	3.79	4.18
MRR	0.52	0.825	0.822	0.806
Hits@1	0.397	0.761	0.755	0.734
Hits@3	0.573	0.869	0.868	0.859
Hits@10	0.787	0.946	0.945	0.943

Relation domain and range completion method based on knowledge graph embedding

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