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A metrics-based meta-learning model with meta-pretraining for industrial knowledge graph construction
Liu, Yangshengyan1,2; Gu, Fu1,2,3; Wu, Yijie2; Gu, Xinjian1,2; 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
ISSN0166-3615
文章类型Article
DOI10.1016/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
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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).
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