At the beginning we needed to convert to RDF form OLU and OTM datasets. Because the OLU dataset depends on objects from other datasets (Corine, Urban Atlas, Cadastral Parcels) it also practically meant to convert all those objects to RDF form also. So ontologies for all those objects have been proposed and after, there were written and run conversion scripts. As we are dealing with huge pan-European datasets, not all the data was converted. We have decided to convert just the data from some selected cities in Czech Republic (centers of NUTS3 regions), Poland (agglomeration areas from Urban Atlas) and Spain (agglomeration areas from Urban Atlas). After we had all data in RDF we started to think about possible queries that could show interlink the data. For instance, here is a map, that shows the OLU polygons colored by the number of SPOI that lie inside them:
http://ng.hslayers.org/examples/olu_spoi/?hs_panel=info&hs_x=1607799.902082933&hs_y=6462976.717926565&hs_z=16&visible_layers=Base%20layer;Land%20use%20parcels

When any OLU polygon is clicked – the information about the polygon and all the SPOI points that lie inside is displayed in a pop-up window.  The pop-up window is browsable i.e. when some object in it is clicked it expands and one can see information about that clicked object.

Also we started to work on some queries that would bring together three mentioned datasets: OLU, OTM and SPOI. For instance, can be query such as: ‘Show me all the land parcels that have hotels and that lie not more than 50 meters away from the major highway?’ . This query does this job. It returns Open Land Use objects that satisfy abovementioned criteria in part of Prague city center around IP Pavlova metro station.

Now we are working on making some graphical interface were user could customize certain parameters from such query (i.e. type of SPOI, type of the road, buffer distance etc).