A Spatiotemporal Ontology for the Administrative Units of Switzerland

Laube for their valuable advice, bright ideas, and numerous contributions. Second, I wish to thank all other WSL staff members that helped me to complete this project. This particularly includes PD Dr. Rolf Grütter for various inputs on ontologies and proofreading the thesis. Furthermore, I sincerely thank Dr. Thomas Scharrenbach for introducing me into Semantic Web software such as Pellet, Protégé, and Virtuoso. Fourth, I would like to thank Elaine Schaefer for proofreading my English writing. Fifth, special thanks to my family for their moral and financial support. Last, I want to thank my friends who also gave me great encouragement to pursue this study. ii Summary The evolution of the administrative units (AUs) of Switzerland has accelerated dramatically in the last 20 years. Between 1990 and 2010, the Federal Statistical Office (FSO) recorded a loss of 427 municipalities, a substantial decrease compared with the decline of 75 municipalities in the 30 years before. This trend implicates an elevated rate of spatial change and makes it difficult for the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) to correctly assign biotope objects to a municipality. Such an accurate reference is necessary because the cantons often entrust the municipalities with the responsibility to protect the biotopes within their boundaries. Hence, the location of a biotope has legal and financial consequences that may change as a result of boundary changes. Despite the relevance of spatial change, there is still a lack of an information system that supports queries on spatial change of municipalities. This stems from the fact that the thematic and the geometric data originate from different sources and do not match. Moreover, AUs can become unrecognizable over time because neither the Historicized Municipality Register (HMR+) nor the geometric datasets give AUs an identity. In order to overcome these difficulties, this thesis set out to determine useful approaches to spatiotemporal databases and ontologies. Such approaches must facilitate the integration of different datasets and support queries on discrete change of objects with crisp boundaries. A review of the literature has found that ontologies fulfill these requirements best, in particular with respect to the integration of different datasets. Using an upper ontology as a common reference framework provides further advantages such as enhanced interoperability and knowledge sharing. While object-centered and event-oriented approaches to spatiotemporal databases focus on a world that comprises objects and events, respectively, the upper ontology Basic Formal Ontology …