Scientific and Technological Resource Sharing Model Based on Few-Shot Relational Learning

Yangshengyan LIU  Fu GU  Yangjian JI  Yijie WU  Jianfeng GUO  Xinjian GU  Jin ZHANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E104-D   No.8   pp.1302-1312
Publication Date: 2021/08/01
Publicized: 2021/04/21
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020BDP0021
Type of Manuscript: Special Section PAPER (Special Section on Computational Intelligence and Big Data for Scientific and Technological Resources and Services)
Category: 
Keyword: 
resource sharing,  scientific and technological resource,  resource graph,  few-shot relational learning,  

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Summary: 
Resource sharing is to ensure required resources available for their demanders. However, due to the lack of proper sharing model, the current sharing rate of the scientific and technological resources is low, impeding technological innovation and value chain development. Here we propose a novel method to share scientific and technological resources by storing resources as nodes and correlations as links to form a complex network. We present a few-shot relational learning model to solve the cold-start and long-tail problems that are induced by newly added resources. Experimentally, using NELL-One and Wiki-One datasets, our one-shot results outperform the baseline framework - metaR by 40.2% and 4.1% on MRR in Pre-Train setting. We also show two practical applications, a resource graph and a resource map, to demonstrate how the complex network helps resource sharing.