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Issue No.01 - Jan.-Feb. (2013 vol.28)
pp: 34-41
Andrzej Uszok , Florida Institute for Human and Machine Cognition (IHMC)
Larry Bunch , Florida Institute for Human and Machine Cognition (IHMC)
Jeffrey M. Bradshaw , Florida Institute for Human and Machine Cognition (IHMC)
Thomas Reichherzer , University of West Florida
James Hanna , US Air Force Research Laboratory Information Directorate
Albert Frantz , US Air Force Research Laboratory Information Directorate
ABSTRACT
The community of interest information-sharing model lets coalition partners publish and disseminate data in a controlled fashion. In this vein, the authors have extended the Phoenix information management system to improve document selection and filtering.
INDEX TERMS
Semantics, OWL, Knowledge management, Publishing, Ontologies, Information management, case-based reasoning, community of interest, policy, ontology, OWL
CITATION
Andrzej Uszok, Larry Bunch, Jeffrey M. Bradshaw, Thomas Reichherzer, James Hanna, Albert Frantz, "Knowledge-Based Approaches to Information Management in Coalition Environments", IEEE Intelligent Systems, vol.28, no. 1, pp. 34-41, Jan.-Feb. 2013, doi:10.1109/MIS.2012.89
REFERENCES
1. A. Uszok et al., “Rapid Creation and Deployment of Communities of Interest Using the CMap Ontology Editor and the KAoS Policy Services Framework,” Proc. 2nd Networked Digital Technologies Conf., Springer, 2010, pp. 451–446.
2. R. Grant et al., “Phoenix: SOA-Based Information Management Services,” Proc. 2009 SPIE Defense Transformation and Net-Centric Systems Conf., 2009; doi:10.1117/12.81 7911.
3. A. Uszok et al., “KAoS Policy Management for Semantic Web Services,” IEEE Intelligent Systems, vol. 19, no. 4, 2004, pp. 32–41.
4. N. Suri et al., “A Dynamic and Policy-Controlled Approach to Federating Information Systems,” Proc. 2010 Military Comm. Conf., IEEE, 2010, pp. 2028–2033.
5. N. Ge, J. Hale, and E. Charniak, “A Statistical Approach to Anaphora Resolution,” Proc. 6th Workshop on Very Large Corpora, 1998, pp. 161–171.
6. D. Gildea and D. Jurafsky, “Automatic Labeling of Semantic Roles,” Computational Linguistics, vol. 28, no. 3, 2002, pp. 245–288.
7. S. Pradhan et al., “Support Vector Learning for Semantic Argument Classification,” Machine Learning, vol. 60, nos. 1–3, 2005, pp. 11–39.
8. G.J.L. Kolodner, Case-Based Reasoning, Morgan Kaufmann, 1993.
9. D.B. Leake, Case-Based Reasoning: Experiences, Lessons, and Future Directions, AAAI Press/MIT Press, 1996.
10. D.B. Leake and D.C. Wilson, “Remembering Why to Remember: Performance-Guided Case-Base Maintenance,” Proc. 5th European Workshop Case-Based Reasoning (EWCBR 2K), LNAI 1898, Springer, 2000, pp. 161–172.
11. B. Smyth, P. Cunningham, and M. Keane, “Hierarchical Case-Based Reasoning: Integrating Case-Based and Decompositional Problem-Solving Techniques for Plant-Control Software Design,” IEEE Trans. Knowledge and Data Eng., vol. 13, no. 5, 2001, pp. 793–812.
12. D. Allemang and J. Hendler, Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL, Morgan Kaufman, 2008.
13. B. DuCharme,Learning SPARQL, O'Reilly Media, 2011.
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