Space debris is a rising problem in today's world. Because there is so much in space that is unknown, it is critical to eventually catalog every piece. Since there are many attributes and properties attached to space objects, it is preferable to use an ontological classification method. The information presented in the ontology can then be used to answer questions about space debris. A Bayesian network would accomplish that because of its quantitative nature. The similarities between ontologies and Bayesian networks, such as their architectures and their flexibility, make it possible to integrate an ontology into a Bayesian network. Image determination and object collision assessment were used as applications to check the viability of integrating ontologies and Bayesian networks. It was determined that ontologies and Bayesian networks are tools that when combined can result in new useful quantitative information.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/613564 |
| Date | January 2016 |
| Creators | Vasilieva, Stephania |
| Contributors | Furfaro, Roberto, Lepore, Robert, Head, Larry K. |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Electronic Thesis |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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