Browse > Article
http://dx.doi.org/10.1633/JISTaP.2021.9.4.3

Knowledge Model for Disaster Dataset Navigation  

Hwang, Yun-Young (Department of Intelligent Data Research, Korea Institute of Science and Technology Information)
Yuk, Jin-Hee (Department of Intelligent Data Research, Korea Institute of Science and Technology Information)
Shin, Sumi (Department of Intelligent Data Research, Korea Institute of Science and Technology Information)
Publication Information
Journal of Information Science Theory and Practice / v.9, no.4, 2021 , pp. 35-49 More about this Journal
Abstract
In a situation where there are multiple diverse datasets, it is essential to have an efficient method to provide users with the datasets they require. To address this suggestion, necessary datasets should be selected on the basis of the relationships between the datasets. In particular, in order to discover the necessary datasets for disaster resolution, we need to consider the disaster resolution stage. In this paper, in order to provide the necessary datasets for each stage of disaster resolution, we constructed a disaster type and disaster management process ontology and designed a method to determine the necessary datasets for each disaster type and disaster management process step. In addition, we introduce a method to determine relationships between datasets necessary for disaster response. We propose a method for discovering datasets based on minimal relationships such as "isA," "sameAs," and "subclassOf." To discover suitable datasets, we designed a knowledge exploration model and collected 651 disaster-related datasets for improving our method. These datasets were categorized by disaster type from the perspective of disaster management. Categorizing actual datasets into disaster types and disaster management types allows a single dataset to be classified as multiple types in both categories. We built a knowledge exploration model on the basis of disaster examples to ensure the configuration of our model.
Keywords
dataset relationships; process-based relationships between datasets; dataset visualization; dataset navigation; knowledge model for dataset navigation; knowledge exploration modeling;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Noy, N. F., Fergerson, R. W., & Musen, M. A. (2000, October 2-6). The knowledge model of Protege-2000: Combining interoperability and flexibility. In R. Dieng, & O. Corby (Eds.), Proceedings of the 12th International Conference, EKAW 2000 (pp. 17-32). Springer.
2 Lee, H. J., Ahn, H. J., Kim, J. W., & Park, S. J. (2006). Capturing and reusing knowledge in engineering change management: A case of automobile development. Information Systems Frontiers, 8(5), 375-394. https://doi.org/10.1007/s10796-006-9009-0   DOI
3 Lin, F.-r., & Yu, J.-H. (2009). Visualized cognitive knowledge map integration for P2P networks. Decision Support Systems, 46(4), 774-785. https://doi.org/10.1016/j.dss.2008.11.020.   DOI
4 Mei, H., Ma, Y., Wei, Y., & Chen, W. (2018). The design space of construction tools for information visualization: A survey. Journal of Visual Languages & Computing, 44, 120-132. https://doi.org/10.1016/j.jvlc.2017.10.001.   DOI
5 Mu, W., Benaben, F., & Pingaud, H. (2018). An ontology-based collaborative business service selection: Contributing to automatic building of collaborative business process. Service Oriented Computing and Applications, 12(1), 59-72. https://doi.org/10.1007/s11761-018-0229-1.   DOI
6 Rao, L., Mansingh, G., & Osei-Bryson, K.-M. (2012). Building ontology based knowledge maps to assist business process re-engineering. Decision Support Systems, 52(3), 577-589. https://doi.org/10.1016/j.dss.2011.10.014.   DOI
7 Sharma, L., & Gera, A. (2013). A survey of recommendation system: Research challenges. International Journal of Engineering Trends and Technology, 4(5), 1989-1992. http://www.ijettjournal.org/volume-4/issue-5/IJETT-V4I5P132.pdf.
8 Wei, K., Huang, J., & Fu, S. (2007, June 9-11). A survey of ecommerce recommender systems. Proceedings of the 2007 International Conference on Service Systems and Service Management (pp. 1-5). Institute of Electrical and Electronics Engineers.
9 Wohlfart, E., Aigner, W., Bertone, A., & Miksch, S. (2008, July 9-11). Comparing information visualization tools focusing on the temporal dimensions. In E. Banissi, L. Stuart, M. Jern, G. Andrienko, F. T. Marchese, N. Memon, R. Alhajj, T. G. Wyeld, R. A. Burkhard, G. Grinstein, D. Groth, A. Ursyn, C. Maple, A. Faiola, & B. Craft (Eds.), Proceedings of the 12th International Conference Information Visualisation (pp. 69-74). Institute of Electrical and Electronics Engineers.
10 Xiong, Q., Wang, Y., Guo, J., & Wu, G. (2008, December 3-5). A searchable knowledge map based on ontology. In H. Zhuge (Ed.), Proceedings of the 4th International Conference on Semantics, Knowledge and Grid (pp. 457-460). Institute of Electrical and Electronics Engineers.
11 Zhang, C., Zhou, G., Lu, Q., & Chang, F. (2017). Graph-based knowledge reuse for supporting knowledge-driven decision-making in new product development. International Journal of Production Research, 55(23), 7187-7203. https://doi.org/10.1080/00207543.2017.1351643.   DOI
12 Savvas, I., & Bassiliades, N. (2009). A process-oriented ontology-based knowledge management system for facilitating operational procedures in public administration. Expert Systems with Applications, 36(3), 4467-4478. https://doi.org/10.1016/j.eswa.2008.05.022.   DOI
13 Guy, I., Zwerdling, N., Carmel, D., Ronen, I., Uziel, E., Yogev, S., & Ofek-Koifman, S. (2009, October 23-25). Personalized recommendation of social software items based on social relations. In L. Bergman, A. Tuzhilin, R. Burke, A. Felfernig, & L. Schmidt-Thieme (Eds.), Proceedings of the 3rd ACM conference on Recommender systems (pp. 53-60). Association for Computing Machinery.
14 Diamond, M., & Mattia, A. (2017). Data visualization: An exploratory study into the software tools used by businesses. Journal of Instructional Pedagogies, 18, 1-7. https://www.aabri.com/manuscripts/162386.pdf.
15 Fox, M. S., & Gruninger, M. (1998). Enterprise modeling. AI Magazine, 19(3), 109. https://doi.org/10.1609/aimag.v19i3.1399.   DOI
16 Coimbra, D. B., Negrao, J. O. M., & Durao, F. A. (2019, October 29-November 1). LODGVis: An interactive visualization for linked open data navigation. In J. dos Santos, D. C. M. Saade, M. d. G. Pimentel, & A. A. Macedo (Eds.), Proceedings of the 25th Brazillian Symposium on Multimedia and the Web (pp. 433-440). Association for Computing Machinery.
17 Dadzie, A.-S., & Pietriga E. (2017). Visualisation of linked data - reprise. Semantic Web, 8(1), 1-21. https://doi.org/10.3233/SW-160249.   DOI
18 Ghosh, A., Nashaat, M., Miller, J., Quader, S., & Marston, C. (2018). A comprehensive review of tools for exploratory analysis of tabular industrial datasets. Visual Informatics, 2(4), 235-253. https://doi.org/10.1016/j.visinf.2018.12.004.   DOI
19 Idreos, S., Papaemmanouil, O., & Chaudhuri, S. (2015, May 31-June 4). Overview of data exploration techniques. In T. Sellis, S. B. Davidson, & Z. Ives (Eds.), Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (pp. 277-281). Association for Computing Machinery.
20 Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370. https://doi.org/10.1023/A:1021240730564.   DOI
21 Keim, D. A., Lee, J. P., Thuraisinghaman, B., & Wittenbrink, C. (1995, October 28). Database issues for data visualization: Supporting interactive database exploration. In A. Wierse, G. G. Grinstein, & U. Lang (Eds.), Proceedings of the IEEE Visualization '95 Workshop (pp. 12-25). Springer.
22 Kim, S., Suh, E., & Hwang, H. (2003). Building the knowledge map: An industrial case study. Journal of Knowledge Management, 7(2), 34-45. https://doi.org/10.1108/13673270310477270.   DOI
23 Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749. https://doi.org/10.1109/TKDE.2005.99.   DOI
24 Pardos, Z. A., & Nam, A. J. H. (2018). A map of knowledge. arXiv. https://arxiv.org/abs/1811.07974v1.
25 Boubaker, A., Milli, H., Leshob, A., & Charif, Y. (2012, March 24-26). A value-oriented approach to business process compensation design. Proceedings of the 2012 International Conference on Information Technology and e-Services (pp. 1-6). Institute of Electrical and Electronics Engineers.
26 Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-Computer Studies, 43(5-6), 907-928. https://doi.org/10.1006/ijhc.1995.1081.   DOI
27 Kopke, J., & Su, J. (2015, August 31-September 3). Towards ontology guided translation of activity-centric processes to GSM. In M. Reichert, & H. Reijers (Eds.), Proceedings of the BPM 2015, 13th International Workshops (pp. 364-375). Springer.
28 Bobadilla, J., Ortega, F., Hernando, A., & Gutierrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109-132. https://doi.org/10.1016/j.knosys.2013.03.012.   DOI
29 AbdEllatif, M., Farhan, M. S., & Shehata, N. S. (2018). Overcoming business process reengineering obstacles using ontology-based knowledge map methodology. Future Computing and Informatics Journal, 3(1), 7-28. https://doi.org/10.1016/j.fcij.2017.10.006.   DOI
30 Abecker, A., Mentzas, G., Legal, M., Ntioudis, S., & Papavassiliou, G. (2001, September 3-7). Business-process oriented delivery of knowledge through domain ontologies. In A. M. Tjoa, & R. R. Wagner (Eds.), Proceedings of the 12th International Workshop on Database and Expert Systems Applications (pp. 442-446). Institute of Electrical and Electronics Engineers.
31 Bikakis, N. (2018). Big data visualization tools. arXiv. https://arxiv.org/abs/1801.08336v2.
32 Christi, J. C. R., & Premkumar, K. (2014, February 27-28). Survey on recommendation and visualization techniques for QOS-aware web services. Proceedings of the 2012 International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-6). Institute of Electrical and Electronics Engineers.
33 Malone, T. W., Crowston, K., & Herman, G. A. (2003). Organizing business knowledge: The MIT process handbook. MIT Press.
34 Marie, N., & Gandon, F. (2014). Survey of linked data based exploration systems. Paper presented at the IESD 2014 - Intelligent Exploitation of Semantic Data, Riva Del Garda, Italy.
35 Ministry of the Interior and Safety. (n.d.). Public Data Portals of Korea. http://www.data.go.kr
36 Mu, W., Benaben, F., & Pingaud, H. (2016). Collaborative process cartography deduction based on collaborative ontology and model transformation. Information Sciences, 334-335, 83-102. https://doi.org/10.1016/j.ins.2015.11.033.   DOI