In the early response phase after the disaster happens, most responders need to find integrated and relevant information to make decision. In this case, finding suitable information in the open crowdsourcing environments is a complex task, since it involves many actors and a large amount of unstructured and heterogeneous spatial data. While quite significant progress on providing system and standardizing syntax heterogeneity of data has been made, semantic issues are still insufficiently addressed. Using ontological approach to represent information in disaster and emergency management can resolve this semantic heterogeneity problem. However, to the best of our knowledge, there is no formal vocabulary or ontology in existence that specifically allow victims to describe incident information in their nature language.
In this thesis, we build a domain ontology model for disaster and emergency information management which is able to capture descriptive spatial information about incidents and reasons them to get more valuable information that mostly needed by disaster responders. On the other hand, we also consider the possibility of integrating our ontology model with the other systems. In order to aim for these two objectives, we propose a methodology to implement hybrid ontological architecture by engaging SUMO (Suggested Upper Merged Ontology); we later complement it with knowledge-based representation that is capable of processing information depending on its content. Building on the result of our proposed ontology implementation, we create several experimental scenarios for information retrieval using our system.

In the early response phase after the disaster happens, most responders need to find integrated and relevant information to make decision. In this case, finding suitable information in the open crowdsourcing environments is a complex task, since it involves many actors and a large amount of unstructured and heterogeneous spatial data. While quite significant progress on providing system and standardizing syntax heterogeneity of data has been made, semantic issues are still insufficiently addressed. Using ontological approach to represent information in disaster and emergency management can resolve this semantic heterogeneity problem. However, to the best of our knowledge, there is no formal vocabulary or ontology in existence that specifically allow victims to describe incident information in their nature language.
In this thesis, we build a domain ontology model for disaster and emergency information management which is able to capture descriptive spatial information about incidents and reasons them to get more valuable information that mostly needed by disaster responders. On the other hand, we also consider the possibility of integrating our ontology model with the other systems. In order to aim for these two objectives, we propose a methodology to implement hybrid ontological architecture by engaging SUMO (Suggested Upper Merged Ontology); we later complement it with knowledge-based representation that is capable of processing information depending on its content. Building on the result of our proposed ontology implementation, we create several experimental scenarios for information retrieval using our system.

1.Hristidis, V., et al., Survey of data management and analysis in disaster situations. Journal of Systems and Software, 2010. 83(10): p. 1701-1714.
2.Mondschein, L.G., The role of spatial information systems in environmental emergency management. Journal of the American Society for Information Science, 1994. 45(9): p. 678-685.
3.R. Neches, R.F., T. Finin, T. Gruber, T. R. Patil, T. Senator, and T. Swartout, Enabling technology for knowledge sharing, in AI Magazine1991.
4.G. W. Lan, X.Q.Z., and Q. Y. Huang. Integration of geographic information services for disaster management. in The International Archieves of the Photogrammetry, Remote Sensing, and Spatial Information Sciences,. 2008.
5.Uschold, M. and M. Gruninger, Ontologies: principles, methods and applications. The Knowledge Engineering Review, 1996. 11(02): p. 93-136.
6.Chin-Lung Chang, Y.-H.C., Tyng-Ruey Chuang, and Andrea Wei-Ching Huang, Connecting the Dots: Tools for Mapping Socio-spatiotemporal Relations in Texts, 2011, Academia Sinica, Taipei, Taiwan.
7.Chang, C.L., et al., Narrative Geospatial Knowledge in Ethnographies: Representation and Reasoning GeoSpatial Semantics, K. Janowicz, M. Raubal, and S. Levashkin, Editors. 2009, Springer Berlin / Heidelberg. p. 188-203.
8.Fan, Z.Z., Sisi. Exploring ontology potential in emergency management. in Proceedings of the Gi4DM Conference - Geomatics for Disaster Management. 2010. Torino, Italy.
9.Little, E.G. and G.L. Rogova. Ontology meta-model for building a situational picture of catastrophic events. in Information Fusion, 2005 8th International Conference on. 2005.
10.P. Kruchten, C.C.W., K. Monu, M. Sotoodeh. A human-centered conceptual model of disasters affecting critial infrastructures. in Proceedings of the 4th International Conference on Information Systems for Crisis Response Management (ISCRAM). 2007. Delft, Netherlands.
11.Sotoodeh, M. and P. Kruchten. An Ontological Approach to Conceptual Modeling of Disaster Management. in Systems Conference, 2008 2nd Annual IEEE. 2008.
12.A. Dilo, S.Z., Spatiotemporal data modeling for disaster management in Netherlands, in Joint ISCRAM-CHINA and GI4DM Conference2008.
13.Iwanaga, I.S.M., et al. Building an earthquake evacuation ontology from twitter. in Granular Computing (GrC), 2011 IEEE International Conference on. 2011.
14.The-Minh, N., et al. Building earthquake semantic network by mining human activity from Twitter. in Granular Computing (GrC), 2011 IEEE International Conference on. 2011.
15.Ushahidi Inc. Platform. [cited 2012 03-15-2012]; Available from: http://www.ushahidi.com.
16.Gruber, T.R., A translation approach to portable ontology specifications. Knowl. Acquis., 1993. 5(2): p. 199-220.
17.Agarwal, P., Ontological considerations in GIScience. International Journal of Geographical Information Science, 2005. 19(5): p. 501-536.
18.Extensible Markup Language (XML). 19-05-2012]; Available from: http://www.w3.org/XML/.
19.Semantic Reasoner. Available from: http://en.wikipedia.org/wiki/Semantic_reasoner.
20.Pease, A.a.C.F., Formal Ontology as Interlingua: The SUMO and WordNet Linking Project, in Ontologies and the Lexicons2007, Cambridge University Press: Cambridge Studies in Natural Language Processing. p. 29-43.
21.Wang, W., et al. Emergency Response Organization Ontology Model and its Application. in Information Science and Engineering (ISISE), 2009 Second International Symposium on. 2009.
22.R., P.A.G.a.B.V. Overview of Knowledge Sharing and Reuse Components: Ontologies and Problem Solving Methods. in Proceedings of the IJCA199 workshop on Ontologies and Problem Solving Methods (KRR5). 1999.
23.Wang, W., C. Dong, and P. Yang. Ontology Modeling of Emergency Plan Systems. in Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on. 2009.
24.Kaneiwa, K., M. Iwazume, and K. Fukuda, An Upper Ontology for Event Classifications and Relations, in AI 2007: Advances in Artificial Intelligence, M. Orgun and J. Thornton, Editors. 2007, Springer Berlin / Heidelberg. p. 394-403.
25.Nathan Morrow, N.M., Adam papendieck, Nicholas Kocmich, Independent Evaluation of the Ushahidi Haiti Project, 2011.
26.Egenhofer, M., A. Frank, and J. Jackson, A Topological Data Model for Spatial Databases Design and Implementation of Large Spatial Databases, A. Buchmann, et al., Editors. 1990, Springer Berlin / Heidelberg. p. 271-286.