Research And Application Of Web Service Data Integration

The weather has great influence on human beings,rich and timely weather information is an essential part of human life.But there are heterogeneous weather Web Service data,mainly in: data sources,data format,data have different expression in different ways,which caused a lot of redundancy and ambiguity.According to the different needs of users,the integration of multi-source weather Web APIs data and the generation of semantic clarity of the data is particularly important.In the face of multi-source heterogeneous Web Service data integration,the key is to solve the problem of semantic mapping between data.Ontology is the basis of semantic web,and it is an important way to conceptualize and model domain knowledge in semantic web.Ontology is widely used in the fields of knowledge representation,artificial intelligence,data integration,information retrieval and so on.Therefore,this paper introduces the construction of weather information domain ontology for the semantic description of multi-source heterogeneous data.Using the ontology modeling tool Karma to model and integrate the "dynamic" weather information data,so as to realize the data sharing and reuse.This paper takes Baidu APIs as the weather data set for the foundation and mainly studies the theory of ontology construction.Through the analysis of multiple data sets and weather information domain concepts,we can extract the relation between concept and concept to construct the ontology of weather information.Based on the data integration tool Karma developed by University of Southern California,we complete the fast modeling of multiple heterogeneous weather Web data sets.Release unified RDF data and using SPARQL query language to verify the correctness of ontology modeling and the correctness of Karma modeling.In this paper,we design and implement a weather information query system based on the standard RDF data set,using Java language and B/S architecture,which includes: the query of the weather data,the time domain statistics and the weather forecast.