## 1 Introduction  
According to the BCC Research Report \"Novel Water Sustainability Technologies: Key Projects and Opportunities, Financing, and Venture Capital, Transactions and Trends\" [1], natural resources and ecology are fundamental to local economies. The growth in demand for natural resources has created a global market based on sustainable technology to harvest maximum supply. The nexus between water and sustainability can be connected via a thorough understanding of water needs, the socio-economic effects of water projects and transfers, and economic and financially efficient water management technologies. With the population explosion (current United Nations estimates forecast 8.5 billion by the year 2030), the society is in dire need of natural resource management. The main goal of sustainable water technology is to make water affordable, accessible and abundant for all human beings in the coming decades. In this new scenario, novel water technologies and tools play a leading role to ensure water policies, at both national and local levels. In this context, an open challenge in modern cities lies in the efficient planning and management of water supply networks, which is evolving from the mere execution of hydrologic engineering projects, to the generation of data-driven approaches for the integrated water management involving multiple sources of data. Emerging Big Data and Linked Open Data (LOD) technologies enable the development of integrated data lakes oriented to the rational management of water resources [2]. The challenge is to integrate multi-source data concerning water management holistically, then leading new and existing tools to present contextual and significant information in an intuitive and interactive way to users, hence aiding in the decision-making process. In general, this is an emerging topic in sustainable development, as it is being applied to multiple related projects such as sustainable tourism [3], educational annotation [4] and green supplier selection [5]. Therefore, from the early design of water monitoring services, a key task is to gather and integrate knowledge from different domains of application, including reservoir strategic levels, environmental and meteorological information, quality measures and registrations, climate change issues, urban planning, end user feedback, energy consumption, etc. [6]. Unfortunately, there are still serious barriers in terms of interoperability that restrict data interchange between these information silos, and limit the analytic power of up-level Business Intelligence (BI) tools. Linked data and Semantic Web technologies are conceived upon protocols and W3C international standards for sharing and annotating structured data on the web. In this context, conceptual modeling is conducted by means of ontologies, which define classes, relationships, instances and axioms of a particular domain of knowledge [7; 8]. In addition, ontology objects refer to concepts in terms of entities and events in the real world, including the relationships between these entities that represent the semantic links. In this context, linking water data to enable semantic knowledge graphs of contextual information is a critical task, since representing water distribution and usage data in the form of linked data repositories would make them open. This will indeed allow for easily combining with external (linked) data from heterogeneous, although relevant, domains. The main motivation of this study is then to design and develop an ontology model to allow knowledge consolidation and extraction in real-world scenarios, where water supply networks are managed with data-driven BI solutions. This paper describes the fundamentals of this proposal, detailing the main components of the initial envisioned architecture, as well as piloting implementation of a water management data workflow, as a proof of concept tool for decision-making strategies.