## 7 Results and Discussion  
The results of mapping the LBCS standard into an ontology seem to be promising. By using this ontology, those involved in urban development processes may identify and classify parcels of an  
Figure 6: How to obtain OWL punned classes. An example of how to obtain the OWL entity (punned class) of a LBCS concept from its numerical code and dimension.  
Figure 7: An individual representing one parcel in the map. The image shows how the world can be “closed” by asserting the cardinality of each property.  
intervention site and semantically annotate them with knowledge regarding the use of the land. This enhances the analysis phase of the planning process by using web ontologies to describe the urban context, thus providing a more intelligent and capable system.  
This paper describes in detail how the LBCS standard can be implemented and how the resulting ontology can be used. It also describes how relational database implementations of LBCS can be converted into the LBCS OWL ontology. It demonstrates that ontologies can be very useful when dealing with land use information and can offer some advantages over the more traditional tools, for example in terms of the inference and consistency checks offered by OWL reasoning.  
However, there are certain aspects that have to be taken into account regarding the use of the LBCS ontology (as with other urban ontologies in real practice scenarios), namely whether those involved in the urban development process need to know how to build axioms and whether they need to know how to build complex queries for the ontology.  
This leads to the need to create an interface for the tool with an associated protocol to facilitate operations within the ontology knowledge. Such a tool is already under development, and is partially described in \"4 CitySemantics, GIS-Semantic Tool for Urban Intervention Areas\" [8]. The basic idea is to develop an urban planning system tool which relies on Semantic Web technologies and incorporates the LBCS ontology, amongst others. The base concept of the tool is defined by top-level system ontologies, namely land use, workflow, and population ontologies. These ontologies, called skeleton ontologies, can be extended by user ontologies. For example, the land use ontology is extended with the categorisation of the LBCS land use standard. Furthermore, the land use is linked to geographic features on an underlying map, which is stored as a shape file.  
The system tool offers a visualisation map, as can be seen in Figure 8, in which the different assertions are coloured according to its classifications. The tool also incorporates a workflow ontology which acts as a guide for the urban planner. Basically, the idea is to design a top ontology which can be extended with user-defined axioms. The goal is to apply Semantic Web techniques to the development of a system for the urban planning process.  
Figure 8: A screenshot of the tool interface. A land use category is selected (left), displaying the features related to this category on the map. Some parcels are selected in the map (right) as the user classifies them with the selected LBCS category.  
In practical terms, this ontology-driven visualisation and classification tool makes a set of editing tasks available that provides those involved in the urban development process with a comprehensive set of features to select, classify, and interpret urban space. The goal is to facilitate the use of all the available information to describe urban space, in order to formulate appropriate design solutions to meet context-driven requirements.