data provided by IoT devices could contribute to the creation of new knowledge about a city. For instance, multiple information are provided by IoT devices installed around a smart city, such as air quality, trafic, noise, etc. In this context, anybody interested in pollution, could not only see the information about pollution but could also navigate towards other factors that can have an impact on pollution, such as road trafic, or that can be impacted by it, such as health, among others. In Figure 1 we report an example as an abstract representation of our proposal to model LOD exploiting MDE techniques. On the left-side (a) we report an example of LOD in which we have three diferent domains, namely Population, Mobility, and Pollution that could be expressed as data objects defined by diferent classes and relationships. In the figure, we report at least one class for each domain representing how the data is linked to each other to enable information sharing. For instance, in the Pollution domain we have a class AirQuality which is linked with a class Health in the Population domain with a relationship (has impact on) that indicates the impact AirQuality has on Health. On the right-side (b) of the ifgure, we report how the example can be modeled by using MDE techniques, by means of the typical structure of data objects in the three domains through metamodeling. As can be seen, we propose to use three models (i.e., Population Model, Pollution Model and Mobility Model) which are conforming to their corresponding metamodels (i.e., Population metamodel, Pollution metamodel and Mobility metamodel). To establish the link between these three models a Weaving Model is introduced. This way, we integrate diferent modeling domains that contributes to the megamodelling by enabling an ecosystem of models.