With this thesis the author intents to contribute to the development of meaningful and machine-interpretable quality descriptions of GI. The work focuses on semantic integrity constraints (SIC). In general, integrity constraints define basic assumptions on the part of real world, which is represented by the data. They enable to detect inconsistencies, that is, unacceptable differences between the data and the data model. SICs are defined as speci c integrity constraints, whose defi ned restrictions are based on the semantics of the modelled entities. They reflect business, legal and other required rules and regulations in the database. For spatial data, many SICs are based on spatial properties like topological or metric relations. Reasoning on such spatial relations and the corresponding derivation of implicit knowledge allow for many interesting applications. Currently the potential of SICs is far from being exploited and SICs are hardly supported by available GISs or spatial database systems. Their effective use mainly requires a formal description of the constraints that enables to transfer and compare the sets of SICs of different data sources. This thesis contributes to the second requirement. Currently, there is no solution for the comparison of SICs pairs and the detection of any conflicts or redundancies in sets of SICs. This also required the inference of implicit restrictions defi ned by the SICs. In consequence, the quality assurance of a data set is possibly more extensive than necessary, because sets of SICs might defi ne redundant restrictions, the integration of SICs sets from multiple data sources is impossible and the assessment of the fi tness for use based on the SICs cannot be supported. These are significant shortcomings for quality assurance and the knowledge sharing within the frame of spatial data infrastructures. Three major contributions are elaborated in the thesis: (i) a detailed categorisation of SICs, (ii) a framework for the formal definition of SICs and (iii) a reasoning methodology for the detection of conflicting and redundant SICs.
(i) The classi cation distinguishes the SICs according to the involved types of spatial and non spatial relation and profoundly differentiates the properties and aspects restricted by spatio-temporal SICs.
(ii) The framework for formal definition of SICs is based on a set of 17 class-level relations. Such qualitative description of cardinality restrictions is novel. The definitions and reasoning rules of the class relations are described independently of oncrete spatial or non-spatial relations, what makes them applicable for many types of SICs.
(iii) The introduced reasoning methodology enables for a detection of conflicts and redundancies in sets of SICs, which has hardly been a research topic before. The overall reasoning algorithm is based on the symmetry, composition and conceptual neighbourhood of class relations.
The feasibility of the proposed algorithm has been veri fied through a prototypical implementation as a plug-in extension of the ontology modelling and knowledge acquisition platform Protege. Possible application areas are quality assurance of geodata, geodata integration and harmonisation, data modelling and ontology engineering, semantic similarity measurements and usability evaluation.