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Disaster Management using Ontology Feature

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Parul Hora, Neha Sheth, Santosh K. Vishwakarma

Parul Hora, Neha Sheth and Santosh K Vishwakarma. Disaster Management using Ontology Feature. International Journal of Computer Applications 183(49):6-9, January 2022. BibTeX

	author = {Parul Hora and Neha Sheth and Santosh K. Vishwakarma},
	title = {Disaster Management using Ontology Feature},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2022},
	volume = {183},
	number = {49},
	month = {Jan},
	year = {2022},
	issn = {0975-8887},
	pages = {6-9},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2022921901},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The digital transformation has witnessed an exponential growth in the recent years. This transformation has touched every instance of human life. The current generation rigorously rely in the platform of information storage & retrieval. This originates an ample opportunity for designing and developing systems for societal benefits. The importance of social networking forums has a vital role in our life. The usage of the above websites is frequent towards maintaining identity, keeping connect with the friends, updating personal & professional information, etc. They have increasingly infused itself into daily life. In recent years, one of the important areas of research is oriented towards from the social networking websites in different categories. This paper represents the work done with an open research dataset known as Microblog Track provided by Forum of Information Retrieval & Evaluation (FIRE). The task provided by the FORUM is to develop a suitable model for the identification of tweets. The training dataset consists of two predefined labels, known as need and availability. In this paper, the prediction rate has been optimized by using the term weighting models before applying the classifiers. The experiments showed that the classification accuracy is increased when the term weight is modified by using the information gain method and using the SVM classifier. This system automatically annotated the FIRE-2015 dataset of microblog track with 97% accuracy.


  1. Antoniou, Natassa, and Mario Ciaramicoli. "Social media in the disaster cycle useful tools or mass distraction?" In International Astronautical Congress. 2013.
  2. Mathbor, Golam M. "Enhancement of community preparedness for natural disasters: The role of social work in building social capital for sustainable disaster relief and management." International Social Work 50, no. 3 (2007): 357-369.
  3. Moumtzidou, Anastasia, Stelios Andreadis, IliasGialampoukidis, Anastasios Karakostas, Stefanos Vrochidis, and IoannisKompatsiaris. "Flood relevance estimation from visual and textual content in social media streams." In Companion Proceedings of the The Web Conference 2018, pp. 1621-1627. International World Wide Web Conferences Steering Committee, 2018.
  4. Murthy, Dhiraj. Twitter. Cambridge, UK: Polity Press, 2018.
  5. Win, Si Si Mar, and ThanNwe Aung. "Target oriented tweets monitoring system during natural disasters." In 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), pp. 143-148. IEEE, 2017.
  6. Basu, Moumita, Saptarshi Ghosh, and Kripabandhu Ghosh. "Overview of the FIRE 2018 track: Information Retrieval from Microblogs during Disasters (IRMiDis)." In Proceedings of the 10th annual meeting of the Forum for Information Retrieval Evaluation, pp. 1-5. ACM, 2018.
  7. Cameron, Mark A., Robert Power, Bella Robinson, and Jie Yin. "Emergency situation awareness from twitter for crisis management." In Proceedings of the 21st International Conference on World Wide Web, pp. 695-698. ACM, 2012.
  8. Neubig, Graham, YuichirohMatsubayashi, Masato Hagiwara, and Koji Murakami. "Safety Information Mining—What can NLP do in a disaster—." In Proceedings of 5th International Joint Conference on Natural Language Processing, pp. 965-973. 2011
  9. Qu, Yan, Chen Huang, Pengyi Zhang, and Jun Zhang. "Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake." In Proceedings of the ACM 2011 conference on Computer supported cooperative work, pp. 25-34. ACM, 2011.
  10. Varga, István, Motoki Sano, KentaroTorisawa, Chikara Hashimoto, KiyonoriOhtake, Takao Kawai, Jong-Hoon Oh, and Stijn De Saeger. "Aid is out there: Looking for help from tweets during a large scale disaster." In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1619-1629. 2013.
  11. Verma, Sudha, Sarah Vieweg, William J. Corvey, LeysiaPalen, James H. Martin, Martha Palmer, Aaron Schram, and Kenneth M. Anderson. "Natural language processing to the rescue? extracting" situational awareness" tweets during mass emergency." In Fifth International AAAI Conference on Weblogs and Social Media. 2011.
  12. TrishnenduGhorai. An information Retrieval System for FIRE 2016 Microblog Track. In working notes of FIRE 2016- Forum for Information Retrieval Evaluation.
  13. Roshni Chakraborty and MaitryBhavsar : Information Retrieval from Microblogs during Disasters FIRE 2016 Microblog Track. In working notes of FIRE 2016- Forum for Information Retrieval Evaluation.
  14. Saptarshi Ghosh and Kripabandhu Ghosh. Overview of the FIRE 2016 Microblog track: Information Extraction from Microblogs Posted during Disasters. In Working notes of FIRE 2016- Forum for Information Ritrieval Evaluation, Kolkata, India, December 7-10, 2016, CEUR Workshop Proceedings., 2016.


Classification, NLP, FIRE, Information Retrieval, tweets; Natural Disaster; social media, disaster monitoring, Microblogging sites, Twitter, Precision, Recall