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Towards an information centric flood ontology for information management and communication

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Abstract

Advancements and new techniques in information technologies are making it possible to manage, analyze and present large-scale environmental modeling results and spatial data acquired from various sources. However, it is a major challenge to make this data accessible because of its unstructured, incomplete and varied nature. Extracting information and making accurate inferences from various data sources rapidly is critical for natural disaster preparedness and response. Critical information about disasters needs to be provided in a structured and easily accessible way in a context-specific manner. This paper introduces a group of information-centric ontologies that encompass the flood domain and describes how they can be benefited to access, analyze, and visualize flood-related data with natural language queries. The presented methodology enables the easy integration of domain knowledge into expert systems and voice-enabled intelligent applications that can be accessed through web-based information platforms, instant messaging apps, automated workflow systems, home automation devices, and augmented and virtual reality platforms. A case study is described to demonstrate the usage of presented ontologies in such intelligent systems.

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References

  • Abburu S, Golla SB (2017) Ontology and NLP support for building disaster knowledge base. In: 2017 2nd International Conference on Communication and Electronics Systems (ICCES). IEEE, pp 98–103

  • Agresta A, Fattoruso G, Pollino M, Pasanisi F, Tebano C, De Vito S, Di Francia G (2014) An ontology framework for flooding forecasting. In: International conference on computational science and its applications. Springer, pp 417–428

  • Apacible JT, Encarnación MJ, Nareddy KR (2013) U.S. patent no. 8,606,739. Washington, DC: U.S. Patent and Trademark Office

  • Babitski G, Probst F, Hoffmann J, Oberle D (2009) Ontology design for information integration in disaster management

  • Beran B, Piasecki M (2009) Engineering new paths to water data. Comput Geosci 35(4):753–760

    Article  Google Scholar 

  • Bermudez LE, Piasecki M (2003) Hydrologic ontology for the web. In: AGU Fall Meeting Abstracts

  • Brank J, Grobelnik M, Mladenic D (2005) A survey of ontology evaluation techniques. In Proceedings of the conference on data mining and data warehouses (SiKDD 2005). Citeseer Ljubljana, Slovenia, pp 166–170

  • De Nicola A, Missikoff M, Navigli R (2009) A software engineering approach to ontology building. Inf Syst 34(2):258–275

    Article  Google Scholar 

  • Demir I, Beck MB, (2009) GWIS: a prototype information system for Georgia watersheds. In Georgia water resources conference: regional water management opportunities, UGA, Athens, GA

  • Demir I, Krajewski WF (2013) Towards an integrated flood information system: centralized data access, analysis, and visualization. Environ Model Softw 50:77–84

    Article  Google Scholar 

  • Demir I, Szczepanek R (2017) Optimization of river network representation data models for web-based systems. Earth and Space Science 4(6):336–347

    Article  Google Scholar 

  • Demir I, Jiang F, Walker RV, Parker AK, Beck MB (2009) Information systems and social legitimacy scientific visualization of water quality. In: EEEE International conference on systems, man and cybernetics, 2009. SMC 2009. IEEE, pp 1067–1072

  • Demir I, Conover H, Krajewski WF, Seo BC, Goska R, He Y, McEniry MF, Graves SJ, Petersen W (2015) Data-enabled field experiment planning, management, and research using cyberinfrastructure. J Hydrometeorol 16(3):1155–1170

    Article  Google Scholar 

  • Demir I, Yildirim E, Sermet Y, Sit MA (2018) FLOODSS: Iowa flood information system as a generalized flood cyberinfrastructure. International Journal of River Basin Management 16(3):393–400

    Article  Google Scholar 

  • Djurić D, Gašević D, Devedžić V, Damjanović V (2005) A UML profile for OWL ontologies. In: Model driven architecture. Springer, Berlin, Heidelberg, pp 204–219

    Chapter  Google Scholar 

  • Dolbear C, Goodwin J, Mizen H, Ritchie J (2005) Semantic interoperability between topographic data and a flood defence ontology. Ordnance Survey Research & Innovation, Southampton

    Google Scholar 

  • Garrido J, Requena I, Mambretti S (2012) Semantic model for flood management. J Hydroinf 14(4):918–936

    Article  Google Scholar 

  • Gray AJG, Galpin I, Fernandes AA, Paton NW, Page K, Sadler J, Kyzirakos K, Koubarakis M, Calbimonte JP, García Castro R, Corcho O (2010) Semsorgrid4env architecture

  • Gruber TR (1995) Toward principles for the design of ontologies used for knowledge sharing? Int J Hum Comput Stud 43(5–6):907–928

    Article  Google Scholar 

  • Guarino N (ed) (1998) Formal ontology in information systems: proceedings of the first international conference (FOIS'98), June 6–8, Trento, Italy, vol. 46. IOS press

  • Guha-Sapir D, Vos F, Below R, Ponserre S (2012) Annual disaster statistical review 2011: the numbers and trends. Centre for Research on the Epidemiology of Disasters (CRED)

  • Holz KP, Hildebrandt G, Weber L (2006) Concept for a web-based information system for flood management. Nat Hazards 38(1–2):121–140

    Article  Google Scholar 

  • Jackson P (1998) Introduction to expert systems. Addison-Wesley Longman Publishing Co., Inc

  • Jones CS, Davis CA, Drake CW, Schilling KE, Debionne SH, Gilles DW, Demir I, Weber LJ (2018) Iowa statewide stream nitrate load calculated using in situ sensor network. JAWRA J Am Water Resour Assoc 54(2):471–486

    Article  Google Scholar 

  • Kalabokidis K, Athanasis N, Vaitis M (2011) OntoFire: an ontology-based geo-portal for wildfires. Nat Hazards Earth Syst Sci 11(12):3157–3170

    Article  Google Scholar 

  • Katuk N, Ruhana Ku-Mahamud K, Norwawi N, Deris S (2009) Web-based support system for flood response operation in Malaysia. Disaster Prevention and Management: An International Journal 18(3):327–337

    Article  Google Scholar 

  • Kogut P, Cranefield S, Hart L, Dutra M, Baclawski K, Kokar M, Smith J (2002) UML for ontology development. Knowl Eng Rev 17(1):61–64

    Article  Google Scholar 

  • Kollarits S, Wergles N, Siegel H, Liehr C, Kreuzer S, Torsoni D, Sulzenbacher U, Papez J, Mayer R, Plank C, Maurer L (2009) ONITOR–an ontological basis for risk management. Tech. rep., MONITOR (January 2009)

  • Kurte KR, Durbha SS, King RL, Younan NH, Potnis AV (2017) A spatio-temporal ontological model for flood disaster monitoring. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, pp 5213–5216

  • Liang TP, Turban E, Aronson JE (2005) Decision support systems and intelligent systems. Penerbit Andi, Yogyakarta

    Google Scholar 

  • Liao SH (2005) Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Syst Appl 28(1):93–103

    Article  Google Scholar 

  • Object Management Group (OMG) (2015) XML Metadata Interchange (XMI) Specification, Version 2.5.1. https://www.omg.org/spec/XMI/2.5.1. Accessed 1 Aug 2019

  • Organization of American States (OAS) (1990). Disaster, planning and development: managing natural hazards to reduce loss. https://www.oas.org/dsd/publications/unit/oea54e/begin.htm#Contents

  • Peppard J, Ward J (2016) The strategic management of information systems: building a digital strategy. Wiley

  • Scheuer S, Haase D, Meyer V (2013) Towards a flood risk assessment ontology–knowledge integration into a multi-criteria risk assessment approach. Comput Environ Urban Syst 37:82–94

    Article  Google Scholar 

  • Sermet Y, Demir I (2018a) An intelligent system on knowledge generation and communication about flooding. Environ Model Softw 108:51–60

    Article  Google Scholar 

  • Sermet Y, Demir I (2018b) Flood action VR: a virtual reality framework for disaster awareness and emergency response training. In: Proceedings of the international conference on modeling, simulation and visualization methods (MSV), pp. 65–68

  • Sit MA, Koylu C, Demir I (2019) Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of hurricane Irma. Int J Digital Earth 1–25

  • Stanford University (2019) Protégé – Products. https://web.archive.org/web/20190430114157/https://protege.stanford.edu/products.php. Accessed 30 April 2019

  • Studer R, Benjamins VR, Fensel D (1998) Knowledge engineering: principles and methods. Data Knowl Eng 25(1):161–198

    Article  Google Scholar 

  • Sui DZ, Maggio RC (1999) Integrating GIS with hydrological modeling: practices, problems, and prospects. Comput Environ Urban Syst 23(1):33–51

    Article  Google Scholar 

  • The NOAA National Severe Storms Laboratory (2018) Severe Weather 101 – Floods. https://www.nssl.noaa.gov/education/svrwx101/floods/types/. Accessed 1 Aug 2019

  • Tripathi A, Babaie HA (2008) Developing a modular hydrogeology ontology by extending the SWEET upper-level ontologies. Comput Geosci 34(9):1022–1033

    Article  Google Scholar 

  • Uschold M, King M (1995) Towards a methodology for building ontologies. Artificial Intelligence Applications Institute, University of Edinburgh, Edinburgh, pp 15–30

    Google Scholar 

  • Wang W, Stewart K (2015) Spatiotemporal and semantic information extraction from web news reports about natural hazards. Comput Environ Urban Syst 50:30–40

    Article  Google Scholar 

  • Weber LJ, Muste M, Bradley AA, Amado AA, Demir I, Drake CW, Krajewski WF, Loeser TJ, Politano MS, Shea BR, Thomas NW (2018) The Iowa watersheds project: Iowa's prototype for engaging communities and professionals in watershed hazard mitigation. International Journal of River Basin Management 16(3):315–328

    Article  Google Scholar 

  • Wu X, Zhu X, Wu GQ, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107

    Article  Google Scholar 

  • Xu J, Nyerges TL, Nie G (2014) Modeling and representation for earthquake emergency response knowledge: perspective for working with geo-ontology. Int J Geogr Inf Sci 28(1):185–205

    Article  Google Scholar 

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Acknowledgements

This project is based upon work supported by the Iowa Flood Center and the University of Iowa.

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Correspondence to Yusuf Sermet.

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Communicated by: H. Babaie

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Sermet, Y., Demir, I. Towards an information centric flood ontology for information management and communication. Earth Sci Inform 12, 541–551 (2019). https://doi.org/10.1007/s12145-019-00398-9

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