A Semantic Web for Weather Forecasting Systems

Weather forecasting is an important application of science for planning our day to day activities. Weather predictions are used in agriculture, air traffic, marine, forestry, severe weather alerts and advisories, military applications and utility companies. The weather forecasting information is provided by various systems with different formats and parameters. As the weather forecasting data are lying in different sources and in heterogeneous format, this cause the data integration cumbersome, 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. This paper focuses on resolving terminological and semantic heterogeneities using a novel ontology aligning algorithm and integration is by ontology merging algorithm. Reference ontology has been developed for aligning, consisting of all possible concepts, attributes and relations for weather forecasting domain to provide knowledge using semantic relations.