mlsanityData to 7 Oct 2026

NASA Science Mission Directorate Knowledge Graph Discovery

arXiv 2303.10871 ↗First posted 20 Mar 2023cs.IR cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 35 references that are in the map. First posted on arXiv 20 Mar 2023.

Builds on

9 of 35 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,404
  3. 2018Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Constructioncited by 321
  4. 2019Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphscited by 106
  5. 2021TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metricscited by 29
  6. 2021Building astroBERT, a language model for Astronomy & Astrophysicscited by 18
  7. 2020Network visualizations with Pyvis and VisJScited by 8
  8. 2022Expanding Knowledge Graphs with Humans in the Loopcited by 3
  9. 2014The Unified Astronomy Thesauruscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.