Ontology based disaster prediction using Animals behavioral changes
J. Vijay Fidelis 1, Sunitha Abburu
Corresponding Author : J. Vijay Fidelis
Department of Computer Applications, Presidency College, Bangalore â€“ 560048, India.
Email ID : email@example.com
Received : 2015-07-11 Accepted : 2015-08-29 Published : 2015-09-11
Abstract : Mother Natureâ€™s blunt and brutal punishment on neglecting or taking for granted her incomparable and exhaustive recourses by either destroying or excessively damaging it. When we make mistakes there should be a person who corrects us in the context of what spoken it is the wrath of Mother Nature to be dealt with. Natural disasters are not of the past but more of the future with regularly occurring phenomenonâ€™s or occurrences which need to be dealt with utmost care and top of priority in list or else existence of human race will be on the brink. The need of hour is one discussed as the basic theme of this paper and being equipped with all possible ways to prevent an exodus of human grave. This paper emphasizes on creating various classes and subclasses and build properties and operations on these classes of which some of the properties which would exactly differ on its functionality aspect of the ones actually intended to be. The behavioral changes would be reflected if there is execution of certain behavioral traits in an object which is well evident from the original one. The classes and subclasses and various other attributes and values will be designed using protÃ©gÃ© tool. And later relevant DL queries can also be applied over it to observe any from any changes exhibited by objects of each class.
Keywords : Semantic, Ontology, RDF, Natural Disaster, Class, behaviors
Citation : J. Vijay Fidelis et al. (2015). Ontology based disaster prediction using Animals behavioral changes. J. of Computation in Biosciences and Engineering. V2I3 DOI : 10.5281/zenodo.XXXXX
Copyright : © 2015 J. Vijay Fidelis. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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