## 4 Use Case
### Water Supply Network Ontology  
One of the main aims of this work is to capture, consolidate and integrate data from different water-related sources. For this reason, we opted to design a semantic approach for data sharing and reconciliation, whereby an agreed ontology model is used to archive a common understanding of the domain in which the system operates. In concrete, we have developed an OWL ontology to describe the indicators used by a water supply company. Important terms in the ontology were obtained directly from the company decision makers. The proposed ontology has a total of 31 classes (groups of individuals sharing the same attributes), 23 object properties (binary relationships between individuals), 2 data properties (individual attributes), 156 logical axioms and 12 individuals. Figure 2 shows the main set of classes (_Zone_, _Indicator_ and _Unit_) in the hierarchy starting from the top class _Thing_ (\(\top\)). These main classes are related to other classes and some of them have subclasses (such as _Zone1_, _Zone2_, _NumberOfWaterSupplies_). The class _Zone_ models the zones where the indicators were measured. The ontology contains eight zones (from _Zone1_ to _Zone8_). Each zone is divided into sub-zones. For example, _Zone3_ has nine sub-zones, from _zone3.1_ to _zone3.9_. In our ontology, sub-zones are modeled as individuals (_zone3.1_, _zone3.2_... _zone3.8_ are individuals of the Class _Zone1_), which allows querying aggregated data of a specific zone. The class _Indicator_ represent formulas for measuring business activities. In our ontology, each _Indicator_ is related to the sub-zone where the value is measured and it has a value with a unit of measure in a corresponding year. The class _Unit_ contains the different unit of measure as individuals (km, m\({}^{3}\), kWh, etc.). Finally, the class _Year_ contains the different years. Technical Details of Water Ontology  
In the following, we describe the main classes, _Indicator_ and _zone_, including some of their most interesting properties. - _Indicator_. Those attributes provided by the water supply company. In this case, indicators represent measures, i.e., known formulas for measuring business activities with no known targets or thresholds. Each Indicator has a value with a unit of measure in a corresponding year (see object and data properties in Table 1). Subclasses of Indicator in the ontology (\(\sqsubseteq\) Indicator) are, among others (see Figure 3), _NumberOfWaterSupplies_, _LeaksTansportNetwork/100 kmNetwork_, _AverageAgeDistribution_, _LengthRenovatedNetwork_, etc. The class _Unit_ contains the different units of measure as individuals, for example: _Km_, _m\({}^{3}\)_ or _number/1000 WaterSupplies_. The class _Year_ contains the different years. Figure 3: Water Ontology: Subclasses of Indicator. Figure 2: Main classes of the water Ontology. Arrows refer to subClassOf. - Zone.This class models the zones where the indicators were measured. The ontology contains eight zones (from _Zone1_ to _Zone8_). Each zone is divided into sub-zones. For example, _Zone2_ has 14 sub-zones, from _zone2.1_ to _zone2.14_. In our ontology, sub-zones are modeled as individuals (_zone2.1, zone2.2... zone2.14_ are individuals of the Class _Zone2_), which allows querying aggregated data of a specific zone. Each indicator is related to the sub-zone where the value is measured. This is modelled by the_hasIndicator_ object property and their subclasses (one for each subclass of _Indicator_, i.e., _hasNumberOfWaterSupplies_, _hasLeaksTansportNetwork/100 kmNetwork_, _hasHydraulicTechnicalPerformanceDistribution_, etc.). Therefore, a concrete zone is related to a subclass of _Indicator_ by means of the corresponding subproperty of _hasIndicator_ and its value, unit of measure and year are specified. Figure 4 shows a graphical representation of these relations for the indicator \"Hydraulic technical performance distribution\", while Table 2 presents the logical axioms used in the ontology.