Weather Phenomenon Prediction Using Semantic Web

Abstract

Weather phenomenon analysis is the application of current technology and science to predict the accurate weather condition for planning our day to day activities. Weather attribute such as temperature, precipitation, wind speed are affected in agriculture, air traffic, marine, forestry, severe weather alerts and advisories, military applications and utility companies. However the process of weather phenomenon is one of the complex areas in meteorology. As weather information is collected from various systems with different formats and parameters. Such type of data is lying in different sources and in heterogeneous format, which are challenging to be integrated in one platform into the knowledge domain. Hence the data source need to be aligned in order to facilitate the smooth integration and to achieve this would involve various processing. There is a need to accomplish unified integration of heterogeneous environments (data sources) and to provide worldwide access to the system. The heterogeneity issues needs to be minimized to arrive a common understanding and decision making by various agencies, research institutions and application areas. In this paper I focus on a novel framework which is proposed for integrating heterogeneous data sources in a single platform, using semantic web techniques. Here I build a ontology which has been developed for aligning, consisting of all possible concepts, attributes and relations for weather phenomenon domain to provide knowledge using semantic relations.