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Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information

Journal Article · · IEEE Transactions on Nuclear Science
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  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
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Detecting and anticipating global proliferation expertise and capability evolution from unstructured, noisy, and incomplete public data streams is a highly desired, but extremely challenging task. Here, in this article, we present our pioneering data-driven approach to support the non-proliferation mission to detect and explain the evolution of proliferation expertise and capability development globally from terabytes of publicly available information (PAI), focusing on our knowledge extraction pipeline and descriptive analytics. We first discuss how we fuse nine open-source data streams, including multilingual data, to convert 4 TB of unstructured data to structured knowledge and encode dynamically evolving proliferation expertise representations—content and context graphs. For this, we rely on natural language processing (NLP) and deep learning (DL) models to perform information extraction, topic modeling, and distributed text representation (aka embedding) learning. We then present interactive, usable, and explainable descriptive analytics to refine domain knowledge and present it in a human-understandable form. Finally, we introduce future work avenues that will leverage our dynamic knowledge representations and descriptive analytics to enable predictive and prescriptive inferences to achieve real-time domain understanding and contextual reasoning about global proliferation expertise and capability evolution.
Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC)
Grant/Contract Number:
AC02-05CH11231; AC05-76RL01830
OSTI ID:
1902232
Alternate ID(s):
OSTI ID: 1961815
Report Number(s):
PNNL-SA-164401
Journal Information:
IEEE Transactions on Nuclear Science, Journal Name: IEEE Transactions on Nuclear Science Journal Issue: 6 Vol. 69; ISSN 0018-9499
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION
annotations
artificial neural networks
big data applications
data collection
data mining
data models
data visualization
decision support systems
knowledge discovery
knowledge representation
natural language processing (NLP)
open source software
prediction models
soft sensors