# NASA Science Mission Directorate Knowledge Graph Discovery

arXiv: https://arxiv.org/abs/2303.10871 · HTML: https://mlsanity.com/paper/2303.10871/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/1908.08210/) (2019)
- [TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics](https://mlsanity.com/paper/2101.10273/) (2021)
- [Building astroBERT, a language model for Astronomy & Astrophysics](https://mlsanity.com/paper/2112.00590/) (2021)
- [Network visualizations with Pyvis and VisJS](https://mlsanity.com/paper/2006.04951/) (2020)
- [Expanding Knowledge Graphs with Humans in the Loop](https://mlsanity.com/paper/2212.05189/) (2022)
- [The Unified Astronomy Thesaurus](https://mlsanity.com/paper/1403.6656/) (2014)

## Built on by (most cited first, 2 of 2)

- [A Position Paper on the Automatic Generation of Machine Learning Leaderboards](https://mlsanity.com/paper/2505.17465/) (2025)
- [MetaLead: A Comprehensive Human-Curated Leaderboard Dataset for Transparent Reporting of Machine Learning Experiments](https://mlsanity.com/paper/2601.22420/) (2026)

## 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.