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Ontologies for Aviation Data ManagementManaging complex aviation data can be a significant challenge for any enterprise – whether a government agency, airline, airframe manufacturer, or aviation service provider. To handle this challenge, data models are typically developed to characterize and manage the data generated, used, and stored by a given enterprise. Unfortunately, different data providers employ qualitatively different data models, and this gives rise to problems exchanging data across organizational boundaries. Over the past decade, these problems have motivated data producers and consumers to look toward standardized data exchange models to address data interoperability. In this paper we examine some of these standardized data exchange models and compare them with a new type of data model based on ontologies. Ontology models have emerged in recent years from a confluence of research in the artificial intelligence, semantic web, and information science communities. This paper introduces ontology models, provides several use cases for ontologies relevant to aviation data management, and summarizes state of the art aviation prototype applications that utilize ontologies.
Document ID
20190000227
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Keller, Richard M.
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
January 31, 2019
Publication Date
September 25, 2016
Subject Category
Air Transportation And Safety
Cybernetics, Artificial Intelligence And Robotics
Report/Patent Number
ARC-E-DAA-TN34754
Report Number: ARC-E-DAA-TN34754
Meeting Information
Meeting: 35th Digital Avionics Systems Conference (DASC)
Location: Sacramento, CA
Country: United States
Start Date: September 25, 2019
End Date: September 30, 2019
Sponsors: Institute of Electrical and Electronics Engineers
Funding Number(s)
WBS: WBS 999182.02.40.01.01
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Keywords
artificial intelligence
air traffic management
data management
data management
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