NASA Science Mission Directorate Knowledge Graph Discovery

TL;DR

This paper presents a pipeline for generating knowledge graphs from NASA SMD data to facilitate dataset discovery and uncover cross-domain connections, leveraging NLP techniques.

Contribution

It introduces a novel pipeline for creating domain-specific knowledge graphs from NASA data, enhancing dataset search and discovery capabilities.

Findings

Created knowledge graphs for NASA SMD domains

Identified cross-domain connections in NASA data

Discussed NLP methods and challenges in KG generation

Abstract

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.