Ontology Applications for Achieving Situation Awareness in Military Decision Support Systems
Common Operational Picture and in consequence Situation Awareness, had been second class issue treated as a result of decision support procedures. This work concentrates mainly on developing methods for integrating battlefield data using ontologies and presenting such data in distributed GIS environment supported by reasoning methods. Such approach allows to organize and filter data using predefined filters dedicated for various level of command and types of operations. Combining semantic data and spatial querying mechanisms, produce extendable and rapid mechanism for battlespace information presentation which vastly increases the speed of decision process. Published results contain analysis of known military domain models. Based on such study, author proposed, a core ontology for representing current battlefield scenario, filled with geospatial information and tactical data gathered from decision support algorithms and military domain analysis. The concept have been applied in presented designed and developed prototype in Service Oriented Architecture demonstrating, a Network Enabled Capability integrated battlespace picture.
KeywordsMilitary operations Network Centric Warfare situation awareness ontology semantic models decision support
Unable to display preview. Download preview PDF.
- 1.NATO, NATO Common Operational Picture (NCOP) - NC3A, 16.11.2006 r Google Scholar
- 2.NATO MIP, The joint C3 information exchange data model overviewGoogle Scholar
- 3.Endsley, M.R., Garland, D.J.: Situation Awareness Analysis and Measurement: Analysis and Measurement. Lawrence Erlbaum Associates, Mahwah (2000)Google Scholar
- 4.Alberts, D., Garstka, J., Stein, F.: Network Centric Warfare: Developing and Leveraging Information Superiority, Library of Congress, 2nd edn. (1999)Google Scholar
- 5.Noy, N.F., Musen, M.A.: PROMPT: Algorithm and tool for automated ontology merging and alignment. In: Proceedings 17th National Conference on Artificial Intelligence, Austin, Texas, USA (2000)Google Scholar
- 7.Doan, A., Madhaven, J., Domingos, P., Halevy, A.: Ontology matching: A machine learning approach. In: Handbook on Ontologies in Information Systems. Springer, Heidelberg (2004)Google Scholar
- 8.Roman, D., Keller, U., Lausen, H., Bruijn, J., Lara, R., Stollberg, M., Polleres, A., Feier, C., Bussler, C., Fensel, D.: Web Service Modeling Ontology. Applied Ontology (2005)Google Scholar
- 9.Trastour, D., Preist, C., Coleman, D.: Using Semantic Web Technology to Enhance Current Business-to-Business Integration Approaches, HP Labs (2008)Google Scholar
- 11.Davies, J., Fensel, D., Harmelen, F.: Towards the Semantic Web: Ontology-driven Knowledge Management. In: HPL-2003-173. John Wiley & Sons, Chichester (2003)Google Scholar
- 12.Herrmann, M., Dalferth, O., Aslam, M.A.: Applying Semantics (WSDL, WSDL-S, OWL) in Service Oriented Architectures (SOA). In: 10th Intl. Protégé Conference (2007)Google Scholar
- 13.Currie, E., Parmelee, M.: Toward s Knowledge-Based Solution for Information Discovery in Complex and Dynamic Domains. In: 7th Intl. Protégé Conference (2004)Google Scholar
- 14.Maedche, A., Staab, S.: Comparing Ontologies— Similarity Measures and a Comparison Study, Internal Report No. 408 (March 2001)Google Scholar