## 4 Use Case
### Application to Water Supply Networks Management  
Following the strategy and use case specifications commented before, a series of steps are now detailed to materialize the semantic model accordingly. **Step 1. ** Data mapping and pre-processing of sources: The specification of data sources is not intended to be exhaustive, but it just describes the most important points. The original XLS data format describes hydro-graphic zones, sub-zones and values, including indicators such as water leakages or structural breaks. The output is a machine-readable structured data file, using non proprietary formats, like CSV (Table 3). In our experiment, most of the data were anonymized due to privacy reasons. **Step 2. ** Data model generation: A parser has been implemented in Java, using Apache Jena ([[https://jena.apache.org/](https://jena.apache.org/)]([https://jena.apache.org/](https://jena.apache.org/)) (visited on 4 February 2019)) as a framework that applies mapping rules between the original dataset and our proposed ontology (classes, properties and relationships) (see the code at [[https://github.com/smartdatau/rdfwater](https://github.com/smartdatau/rdfwater)]([https://github.com/smartdatau/rdfwater](https://github.com/smartdatau/rdfwater)) (visited on 4 February 2019)). As a result, the RDF file is obtained containing all the original data mapped to the domain ontology specifying the classes, properties, and relationships. The output dataset has been obtained following the pattern catalogue for modelling Linked Data [45]. Table 4 shows how the path to the resources is defined using explicit descriptions of the entities. The dots represent the common water prefix, while the asterisks represent a particular value. Table 5 describes the prefixes and the used namespaces in the dataset. \begin{table}
\begin{tabular}{l l} \hline \hline
**Entity** & **Pattern** \\ \hline Zone &.../zone/* \\ Water supply &.../hasNumberOfWaterSupplies/* \\ Water leaks &.../hasLeaksDistributionNetwork/* \\ Water not recorded &.../hasWaterNotRecorded/* \\ Length supply network &.../hasLengthSupplyNetwork/* \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Design patterns for the entities of the water supply company domain. Figure 5: Example of graph representation according to the water Ontology. **Step 3. ** Data storage: Several options to provide SPARQL access to the RDF storage were evaluated, including OpenLink Virtuoso ([[http://virtuoso.openlinksw.com](http://virtuoso.openlinksw.com)]([http://virtuoso.openlinksw.com](http://virtuoso.openlinksw.com)) (visited on 4 February 2019)), Eclipse RDF4J ([[http://www.rdf4j.org/](http://www.rdf4j.org/)]([http://www.rdf4j.org/](http://www.rdf4j.org/)) (visited on 4 February 2019)) and Stardog ([[https://www.stardog.com/](https://www.stardog.com/)]([https://www.stardog.com/](https://www.stardog.com/)) (visited on 4 February 2019)). The last one was selected in order to implement the access to the data, since it satisfied the requirements by supporting integrity constraint validation, batch indexing and reasoning. As previously mentioned, to facilitate data reuse and interoperability, links to external resources have been defined. LOD repositories based specifically on the Valencian Community are limited to some extent, but some approaches such as datos.ign.es (datos.ign.es is an initiative accessible at the European Data Portal to provide the original geographic information from the Spanish Instituto Geografico Nacional as LOD. [[http://datos.ign.es/](http://datos.ign.es/)]([http://datos.ign.es/](http://datos.ign.es/)) (visited on 19 December 2019)) provide geographic information as LOD. However, cross domain repositories and international geographic datasets can be used in this sense. Therefore, the zones and subzones were manually linked to Wikidata using the _owl:sameAs_ relationship (see, for example, the example in Listing 1). These links contribute to the rich connectivity promoted by LOD. Finally, the data exportation to RDF (for instance, N3) is carried out.