知识图谱嵌入中的自适应筛选

doi:10.13229/j.cnki.jdxbgxb20180791

Abstract

Abstract:

Due to the imbalance of KG data and the difficulty of training, that random sampling of training data may make it difficult for embedded models to converge rapidly. Therefore, in this paper, an adaptive method for sampling of training data is proposed. The training data are grouped according to the different relationships. In the sampling process, a group is determined according to the probability, and then an instance is randomly selected from the determined group for training. At the same time, according to the training effect, the probability of each selected instance is adjusted adaptively. Experimental results show that adaptive grouping filter achieves better results in link prediction tasks, and enables the embedded model to converge faster and better.

Key words: artificial intelligence, knowledge graph embedding, translation-based embedding models, adaptive sampling, link prediction

CLC Number:

TP391

Dan-tong OUYANG,Cong MA,Jing-pei LEI,Sha-sha FENG. Knowledge graph embedding with adaptive sampling[J].Journal of Jilin University(Engineering and Technology Edition), 2020, 50(2): 685-691.

Figures/Tables 4

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数据集	实体数	关系数	训练集	验证集	测试集
FB15k	14 951	1 345	483 142	50 000	59 071
WN18	40 943	18	141 442	5 000	5 000
FB15k-237	14 541	237	272 115	17 535	20 466

数据集	Metric	TransE	AST	TransE_NZL	AST_NZL
FB15k	Mean Rank	142	134	144	117
FB15k	Hits@10	0.714	0.716	0.733	0.795
WN18	Mean Rank	490	457	456	425
WN18	Hits@10	0.932	0.939	0.926	0.946
FB15k-237	Mean Rank	252	255	319	308
FB15k-237	Hits@10	0.422	0.423	0.443	0.458

Knowledge graph embedding with adaptive sampling

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