  The size of the National Aeronautics and Space Administration (NASA) Science
Mission Directorate (SMD) 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.
