Zobrazeno 1 - 10
of 117
pro vyhledávání: '"Kuansan Wang"'
Autor:
Jean-Claude Burgelman, Kuansan Wang
Publikováno v:
Frontiers in Research Metrics and Analytics, Vol 8 (2023)
Externí odkaz:
https://doaj.org/article/4f8748ed55fb4d8aaad02c3f046a5cc1
Publikováno v:
Frontiers in Big Data, Vol 3 (2021)
Externí odkaz:
https://doaj.org/article/f1e88339d9c840dd9738fe6573870a3d
Autor:
Anshul Kanakia, Kuansan Wang, Yuxiao Dong, Boya Xie, Kyle Lo, Zhihong Shen, Lucy Lu Wang, Chiyuan Huang, Darrin Eide, Sebastian Kohlmeier, Chieh-Han Wu
Publikováno v:
Frontiers in Research Metrics and Analytics, Vol 5 (2020)
On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering sy
Externí odkaz:
https://doaj.org/article/d101f9223242447b988af9a2fcd79a64
Publikováno v:
Nature Human Behaviour. 6:1344-1350
Knowledge of how science is consumed in public domains is essential for understanding the role of science in human society. Here we examine public use and public funding of science by linking tens of millions of scientific publications from all scien
Autor:
Vinay K. Chaudhri, Chaitanya Baru, Naren Chittar, Xin Luna Dong, Michael Genesereth, James Hendler, Aditya Kalyanpur, Douglas B. Lenat, Juan Sequeda, Denny Vrandečić, Kuansan Wang
Publikováno v:
AI Magazine; Vol. 43 No. 1: Spring 2022; 17-29
Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beginning to play a central role in representing infor
Autor:
Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Evgeny Kharlamov, Bin Shao, Rui Li, Kuansan Wang
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-14
Publikováno v:
IEEE Transactions on Emerging Topics in Computing. 9:200-203
The papers in this special section focus on Big Data as it applies to scholarly information - or scholarly Big Data. This represents the vast quantity of research output, which can be acquired from digital libraries, such as journal articles, confere
Autor:
Yu Zhang, Zhihong Shen, Chieh-Han Wu, Boya Xie, Junheng Hao, Ye-Yi Wang, Kuansan Wang, Jiawei Han
Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set. Most existing LMTC approaches rely on massive human-annotated training data, which are often costly to obtain and suf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2062b83cc113ca4c9b294567f01e2480
http://arxiv.org/abs/2202.05932
http://arxiv.org/abs/2202.05932
Publikováno v:
Quantitative Science Studies, Vol 1, Iss 1, Pp 396-413 (2020)
An ongoing project explores the extent to which artificial intelligence (AI), specifically in the areas of natural language processing and semantic reasoning, can be exploited to facilitate the studies of science by deploying software agents equipped
Knowledge of how science is consumed in public domains is essential for a deeper understanding of the role of science in human society. While science is heavily supported by public funding, common depictions suggest that scientific research remains a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a9aece57dfaa4c953ac05fee154d20d
https://doi.org/10.3386/w28748
https://doi.org/10.3386/w28748