Observation of L'Aquila Toads in ponds, to Forecast Earthquakes using Semantic web and Ontology
J. Vijay Fidelis , Sunitha Abburu
Corresponding Author : J. Vijay Fidelis,
Presidency College, Bangalore.
Email ID : firstname.lastname@example.org
Received : 2014-07-11 Accepted : 2014-08-02 Published : 2014-08-12
Abstract : Natural disasters have been always referred to as Mother Natures blow to today's unconventional way of living. Once there is Tsunami and on the other hand earthquakes which are termed mass exodus of life. One of the key instance of proposing the paper are that we cannot challenge mother nature but at least come to know when she really might throw a blow on this earth. Mother Nature might provide us the clues which are far more difficult for us as human beings to interpret. One of the key features of this paper is how animals might not help us overcoming Natural disaster but to safeguard over selves from the onslaught of Mother Nature if there were any possibilities. The history of artificial intelligence shows that knowledge is very critical for intelligent systems to be optimized. In many cases, better knowledge can be more important for solving a task than better algorithms. To have truly intelligent systems, knowledge needs to be captured, processed, reused, and communicated. Ontologies support all these tasks which enable us to take critical decisions at time of need.
Keywords : Semantics, Ontology, Intelligent systems, Toads, Knowledge base.
Citation : J. Vijay Fidelis, et al.. (2014). Observation of L'Aquila Toads in ponds, to Forecast Earthquakes using Semantic web and Ontology. J. of Computation in Biosciences and Engineering. V1I3. DOI : 10.5281/zenodo.999915
Copyright : © 2014 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.
Journal of Computation in Biosciences and Engineering
ISSN : 2348-7321
Volume 1 / Issue 3
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