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Enhancing Dataset Discovery With Knowledge Graph Link Prediction Techniques ● In the evolving landscape of open science, the ability to navigate and discover pertinent datasets is increasingly significant. This primarily hinges on the presence of detailed metadata, delineating the dataset’s content, and potential spheres of application.

● The GES DISC datasets are characterized by science keywords to enable dataset discovery in web search interfaces.

● A problem may arise where a dataset lacks a science keyword that it otherwise should have.

● Machine learning techniques such as link prediction can be used to detect these missing science keywords by estimating the probability of new links forming between dataset and keyword nodes.
Document ID
20240000785
Acquisition Source
Goddard Space Flight Center
Document Type
Poster
Authors
Sean Hughes
(University of Maryland, College Park College Park, United States)
Irina Gerasimov
(Adnet Systems (United States) Bethesda, Maryland, United States)
Armin Mehrabian
(Adnet Systems (United States) Bethesda, Maryland, United States)
Long Pham
(Goddard Space Flight Center Greenbelt, United States)
Date Acquired
January 18, 2024
Subject Category
Computer Programming and Software
Meeting Information
Meeting: 104th American Meteorological Society (AMS) Annual Meeting
Location: Baltimore, MD
Country: US
Start Date: January 28, 2024
End Date: February 1, 2024
Sponsors: American Meteorological Society
Funding Number(s)
CONTRACT_GRANT: 80GSFC17C0003
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
NASA Peer Committee
Keywords
machine learning
knowledge graphs
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