A metrics-based meta-learning model with meta-pretraining for industrial knowledge graph construction

doi:10.1016/j.compind.2022.103753

CASISD OpenIR > 智库建设研究部

	A metrics-based meta-learning model with meta-pretraining for industrial knowledge graph construction
	Liu, Yangshengyan 1,2; Gu, Fu 1,2,3; Wu, Yijie 2; Gu, Xinjian 1,2; Guo, Jianfeng4,5
发表期刊	COMPUTERS IN INDUSTRY
关键词	Industrial knowledge graph Few-shot text classification Meta-learning Deep metric learning Attribute-based fusion
摘要	Isolated data silos and domain-specific knowledge pose challenges for knowledge graph construction in the manufacturing industry, where heterogeneous storage leads to distributed databases with complex schemas. In this article, a resource-based industrial knowledge graph is developed using a few-shot classification algorithm to save on labor and other related costs in industrial knowledge graph construction, and an attribute-based fusion strategy for data fusion and alignment is designed. We also propose a novel metrics-based meta-learning model with meta-pretraining (MMM) to address the few-shot text classification problem. Experiment results show that MMM achieves 87.13% accuracy on the 5-shot text classification benchmark Amazon Review Sentiment Classification (ARSC), outperforming other baselines, such as Induction Networks (85.63%) and Distributional Signatures (81.16%). The MMM achieves a 34.6% accuracy improvement compared with Distributional Signatures (84.34% vs. 62.66%) on 1-shot problems of ARSC, hence highlighting the applicability of our model in low-resource conditions. Based on the proposed methods, we further develop an industrial knowledge graph platform with industrial applications, such as value chain analysis and collaboration, to improve knowledge reuse and service innovation.
	2022
卷号	143
ISSN	0166-3615
文章类型	Article
DOI	10.1016/j.compind.2022.103753
关键词[WOS]	CLASSIFICATION
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000930943800001
引用统计
文献类型	期刊论文
条目标识符	http://ir.casisd.cn/handle/190111/12080
专题	智库建设研究部
作者单位	1.Zhejiang Univ, State Key Lab Fluid Power & Mechatron Syst, Hangzhou 310027, Peoples R China 2.Zhejiang Univ, Key Lab Adv Mfg Technol Zhejiang Prov, Hangzhou 310027, Peoples R China 3.Zhejiang Univ, Dept Ind & Syst Engn, Hangzhou 310027, Peoples R China 4.Zhejiang Univ, Ctr Engn Management, Polytech Inst, Hangzhou 310015, Peoples R China 5.Univ Chinese Acad Sci, Sch Publ Policy & Management, Beijing 100049, Peoples R China 6.Chinese Acad Sci, Inst Sci & Dev, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yangshengyan,Gu, Fu,Wu, Yijie,et al. A metrics-based meta-learning model with meta-pretraining for industrial knowledge graph construction[J]. COMPUTERS IN INDUSTRY,2022,143.
APA	Liu, Yangshengyan,Gu, Fu,Wu, Yijie,Gu, Xinjian,&Guo, Jianfeng.(2022).A metrics-based meta-learning model with meta-pretraining for industrial knowledge graph construction.COMPUTERS IN INDUSTRY,143.
MLA	Liu, Yangshengyan,et al."A metrics-based meta-learning model with meta-pretraining for industrial knowledge graph construction".COMPUTERS IN INDUSTRY 143(2022).