## 2 Background and Related Work
### Related Work  
Providing information about water consumption and water availability will increase end-user awareness and improve the quality of water management decisions and water government [6]. In addition, improving access to data and fostering open exchange of water information are crucial to solving water resources issues [17].  
With respect to the massive volumes of data, as it occurs with water information, they are already present in modern cities and still rapidly growing as a result of diverse data sources, including all types of smart devices and sensors (Internet of Things, IoT) and social networks. This fact has led to an increasing interest in incorporating these huge amounts of external and unstructured data into traditional applications. However, the potential opportunity offered by the gathered information is often not exploited due to advanced techniques of data analysis, visualization and services to enable data exploration have to be developed. In this regard, the use of specific ontologies in smart city domains, such as Smart City Ontology (SCO) [18; 19], allows a semantics-enabled exploration of urban data.  
The publication of ontologies related to water management has aroused great interest in the research community. Water management in smart cities is often focused on providing the adequate water supply to the citizens [20]. Several approaches are focused on ontology frameworks for effective management of water, taking into account aspects such as quality, efficiency of water reuse, or detection of failures [21; 22]. In addition, a model is introduced with the objective of integrating water distribution networks, including a review of the most well-known related ontologies [23].  
Nowadays, many public and governmental institutions are investigating different ways to make visible their data and publish them as LOD. Furthermore, they are developing new interfaces to facilitate the interaction with general users for exploring data. However, many aspects must be taken into account in order to exploit the full potential of the data.  
With regard to the enrichment process, several open Knowledge Graphs (KGs) have been created based on LOD concepts, such as DBpedia [24], Wikidata [25] and YAGO [26]. KGs are a rich source of information covering general knowledge to enrich original datasets. They provide structured information which allows: (i) the connections with other external repositories (e.g., GeoNames), and (ii) multilingual aspects through the access to descriptions and properties in different languages. In addition, for a dataset to be useful, it is crucial that the data be valid and consistent [27]. On the other hand, The European Data Portal ([[https://www.europeandataportal.eu](https://www.europeandataportal.eu)]([https://www.europeandataportal.eu](https://www.europeandataportal.eu)) (visited on 19 December 2019)) harvests the data collections available on public data portals across European countries. According to the Reusing Open Data report [28], geographic data are the second data category most reused by institutions in the member states of the European Union.  
Moreover, the usage of reliable standards promoted by known organizations for data publication enable easy reuse. The combination of different datasets facilitates the creation of multidimensional models in which different evaluation techniques can be applied. It is important to mention that W3C recommends the RDF Data Cube Vocabulary [29] for the publication of multidimensional data.  
We can conclude this section after reviewing previous work that, to the best of our knowledge, our general framework is the first one which allows for modelling, generating, publishing and exploiting a dataset related to water management domain. Furthermore, general users can visualize and analyse the enriched data with the links to external repositories.