The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
자원 공유는 수요자가 필요한 자원을 사용할 수 있도록 보장하는 것입니다. 그러나 적절한 공유 모델이 부족하여 현재 과학기술 자원의 공유율이 낮아 기술 혁신과 가치 사슬 발전을 방해하고 있습니다. 여기서 우리는 복잡한 네트워크를 형성하기 위해 자원을 노드로 저장하고 상관 관계를 링크로 저장하여 과학 기술 자원을 공유하는 새로운 방법을 제안합니다. 새로 추가된 리소스로 인해 발생하는 콜드 스타트 및 롱테일 문제를 해결하기 위한 퓨샷 관계형 학습 모델을 제시합니다. 실험적으로 NELL-One 및 Wiki-One 데이터 세트를 사용하여 일회성 결과는 기준 프레임워크(metaR은 40.2%, MRR은 4.1%)보다 뛰어납니다. 사전 훈련 환경. 또한 복잡한 네트워크가 리소스 공유에 어떻게 도움이 되는지 보여주기 위해 두 가지 실제 응용 프로그램인 리소스 그래프와 리소스 맵을 보여줍니다.
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
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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
부
@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},}
부
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 -