Data mining for ontology development.

Davidson, George S.; Strasburg, Jana D.; Stampf, David; Neymotin, L.; Czajkowski, C.J.; Shine, E. P.; Bollinger, James S.; Ghosh, V.J.; Sorokine, Alexandre; Ferrell, Regina; Ward, Richard C.; Schoenwald, David A.

doi:10.2172/992328

Cited by 1 publication

(1 citation statement)

References 2 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This report documents the definition of events of interest and a hierarchical structure that will be used in the forecasting system. This report will also guide the research team in development or use of existing semantic dictionaries that are instrumental to searching, parsing, and categorizing events [1] [2] [3]. In addition, various geolocations around the United States are identified for sensitivity and null case testing.…”

Section: Introductionmentioning

confidence: 99%

Event Definition for the Automated Detection of Nuclear Proliferation Activities

Danielson

Kail

Pike

2020

View full text Add to dashboard Cite

In FY2020, Savannah River National Laboratory (SRNL) in collaboration with the Discovery Analytics Center (DAC) at Virginia Polytechnic Institute and State University (VT) began developing a demonstration prototype system that uses multiple machine learning and data analytic methods on largescale open data sources to identify new, developing, or undeclared nuclear programs. One of the most challenging aspects of applying machine learning techniques to such a problem is the high likelihood of extremely sparse data from disparate sources. To overcome this challenge, the current work will use a strategic combination of supervised, semi-supervised, and unsupervised learning techniques to ingest and fuse data streams to make a forecast of nuclear activities in a targeted geospatial location. Identifying potential data sources and training supervised learning algorithms is dependent upon the development of a robust foundation of targeted event domains that fundamentally define the nuclear activities of interest. This report documents the definition of a hierarchical structure for both nuclear activity and event domains that will be used to guide the research team in development or use of existing semantic dictionaries that are instrumental to searching, parsing, and categorizing events for the forecasting system's use.

show abstract

Section: Introductionmentioning

confidence: 99%