The emergence of Web 2 and distributed resources has provided a unique opportunity for volunteers to produce and share spatial data on the web. This opportunity also has some downsides such as data integration problems caused by data heterogeneity. In this project, we are striving to tackle the data integration problem by proposing a novel approach for road network data structuring using Resource Description Framework (RDF) data model. Using such a data model, the semantics and logical relationships among features in a dataset can be stored, which can then be used to extract further knowledge from data. This knowledge will then be used to improve the data integration. As the road network data is usually big and complex, the ontology inference is very time consuming and may be impossible on desktop computers with limited memory and processing power. Therefore, to test the validity of the proposed data model, we intend to run the ontology reasoning on a more powerful computer.