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Local administrations generate large quantities of data due to the processes followed to attend administrative governance issues and the needs of its citizenry. Sadly, in most cases this data is not fully exploited and remains within the institutions, making their reutilization very difficult. Currently, open data initiatives have gained ground worldwide and more cities are taking advantage of adopting an open data strategy, which are visible at the organizational level and at user level. In this respect, there is a need to generate guidelines that allow cities: (a) to identify datasets to be shared, for example pollution, commercial premises, public services, etc. and (b) to publish quality data on their portals. Data should be accurate and interoperable among cities to facilitate reuse. This work describes the development process of an ontology to represent the acoustic pollution data collected by measurement stations located in cities, providing a common model for data publication. The developed ontology reuses several well-known ontologies and includes classes, properties and instances specifically created to cover this domain. This work also includes real examples about how to instantiate the ontology.

We describe the process and tools that we have used to generate and publish the BTN100 Linked Dataset, based on the original data from the Spanish Topographic Base (1:100.000 scale) from the Spanish Instituto Geográfico Nacional. We have taken into account the limitations and lessons learned from our initial experience on the generation and publication of Linked Data from a range of geographical sources in Spain, in 2010, and we have now refined the process in order to facilitate: declarative mappings for the transformations from existing open data (shapefiles), automation of transformations whenever there are changes in the original data sources, version control, and alignment with INSPIRE URIs. As a result of this transformation and publication process we have also updated the reference ontology for geographical features and aligned with general ontologies such as GeoSPARQL.