NASA Science Mission Directorate Knowledge Graph Discovery

被引:0
|
作者
Timmer, Roelien C. [1 ]
Khoo, Fech Scen [2 ]
Mark, Megan [3 ]
Ribeiro Martins, Marcella Scoczynski [4 ]
Berea, Anamaria [5 ]
Renard, Gregory [6 ]
Bugbee, Kaylin [7 ]
机构
[1] Univ New South Wales, Kensington, NSW, Australia
[2] Carl von Ossietzky Univ Oldenburg, Oldenburg, Germany
[3] Florida Inst Technol, Melbourne, FL 32901 USA
[4] Fed Univ Technol, Curitiba, Parana, Brazil
[5] George Mason Univ, Fairfax, VA 22030 USA
[6] Appl AI Co AAICO, Cupertino, CA USA
[7] NASA, Marshall Space Flight Ctr, Huntsville, AL USA
关键词
D O I
10.1145/3543873.3587585
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The size of the National Aeronautics and Space Administration (NASA) Science Mission Directorate (SMD) data catalog is growing exponentially, allowing researchers to make discoveries. However, making discoveries is challenging and time-consuming due to the size of the data catalogs, and as many concepts and data are indirectly connected. This paper proposes a pipeline to generate knowledge graphs (KGs) representing different NASA SMD domains. These KGs can be used as the basis for dataset search engines, saving researchers time and supporting them in finding new connections. We collected textual data and used several modern natural language processing (NLP) methods to create the nodes and the edges of the KGs. We explore the cross-domain connections, discuss our challenges, and provide future directions to inspire researchers working on similar challenges.
引用
收藏
页码:795 / 799
页数:5
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