IEICE TRANSACTIONS on Information
Scientific and Technological Resource Sharing Model Based on Few-Shot Relational Learning
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.
- Publication
- IEICE TRANSACTIONS on Information 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
Authors
Yangshengyan LIU
Zhejiang University
Fu GU
Zhejiang University
Yangjian JI
Zhejiang University
Yijie WU
Zhejiang University
Jianfeng GUO
University of Chinese Academy of Sciences,Chinese Academy of Sciences
Xinjian GU
Zhejiang University
Jin ZHANG
Zhejiang University
Keyword
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Yangshengyan LIU, Fu GU, Yangjian JI, Yijie WU, Jianfeng GUO, Xinjian GU, Jin ZHANG, "Scientific and Technological Resource Sharing Model Based on Few-Shot Relational Learning" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 8, pp. 1302-1312, August 2021, doi: 10.1587/transinf.2020BDP0021.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020BDP0021/_p
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@ARTICLE{e104-d_8_1302,
author={Yangshengyan LIU, Fu GU, Yangjian JI, Yijie WU, Jianfeng GUO, Xinjian GU, Jin ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Scientific and Technological Resource Sharing Model Based on Few-Shot Relational Learning},
year={2021},
volume={E104-D},
number={8},
pages={1302-1312},
abstract={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.},
keywords={},
doi={10.1587/transinf.2020BDP0021},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Scientific and Technological Resource Sharing Model Based on Few-Shot Relational Learning
T2 - IEICE TRANSACTIONS on Information
SP - 1302
EP - 1312
AU - Yangshengyan LIU
AU - Fu GU
AU - Yangjian JI
AU - Yijie WU
AU - Jianfeng GUO
AU - Xinjian GU
AU - Jin ZHANG
PY - 2021
DO - 10.1587/transinf.2020BDP0021
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2021
AB - 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.
ER -
English
