Scientific Knowledge Graph-driven Research Profiling-Reference-Cited by-同舟云学术

Scientific Knowledge Graph-driven Research Profiling

Published:2022-10-21 Issue: Volume: Page:
ISSN:
Container-title:The 6th International Conference on Computer Science and Application Engineering
language:
Short-container-title:

Author:

Li Jiao¹^ORCID,Sun Tan²^ORCID,Xian Guojian³^ORCID,Huang Yongwen¹^ORCID,Zhao Ruixue³^ORCID

Affiliation:

1. Agricultural Information Institute of CAAS, China

2. Chinese Academy of Agricultural Sciences, China and Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, China

3. Agricultural Information Institute of CAAS, China and Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, China

Funder

Central Public-interest Scientific Institution Basal Research Fund

Optimization and Integration of Key Technologies and Development of A Next Generation of Open Knowledge Service System

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3565387.3565423

Reference24 articles.

1. Generating knowledge graphs by employing Natural Language Processing and Machine Learning techniques within the scholarly domain

2. Elsevier. 2016. Trust in Research. Retrieved January 1, 2022 from https://www.elsevier.com/__data/assets/pdf_file/0011/908435/Trust_evidence_report_summary_Final.pdf. Elsevier. 2016. Trust in Research. Retrieved January 1, 2022 from https://www.elsevier.com/__data/assets/pdf_file/0011/908435/Trust_evidence_report_summary_Final.pdf.

3. Physical activity in the context of the COVID-19 pandemic: Research profiling and mapping;Tomanek M;Physical Education of Students,2021

4. Qualitative Research in Social Sciences: A Research Profiling Study;Bozkurt M;Educational Policy Analysis and Strategic Research,2022

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