Exploiting Linked Spatial Data and Granularity Transformations

Geographic information is one of the fundamental core data sources for various applications. Freely available geographic information knowledge bases are emerging and the spatial dimension has become part of the Linked Open Data initiative. However, geographic information is stored as abstract geographic objects and exploring, extracting, and understanding the information must be facilitated for different user perspectives and use cases. We propose to use a semantic model and an extraction methodology which is aimed at allowing the consumption of geographic information in an intuitive way. We illustrate our approach based on previous work of a highway navigation conceptualization and present a functional approach to exploit granularity extractions targeted at enabling the user to change the point of view in navigation tasks.