A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Anchoring data quality dimensions in ontological foundations
Communications of the ACM
Ontology-driven geographic information systems
Proceedings of the 7th ACM international symposium on Advances in geographic information systems
Space, time, matter and things
Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
Sweetening Ontologies with DOLCE
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
What Are Sports Grounds? Or: Why Semantics Requires Interoperability
INTEROP '99 Proceedings of the Second International Conference on Interoperating Geographic Information Systems
Distinctions Produce a Taxonomic Lattice: Are These the Units of Mentalese?
Proceedings of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006)
Towards A Realism-Based Metric for Quality Assurance in Ontology Matching
Proceedings of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006)
Improving financial data quality using ontologies
Decision Support Systems
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Data quality and ontology are two of the dominating research topics in GIS, influencing many others. Research so far investigated them in isolation. Ontology is concerned with perfect knowledge of the world and ignores so far imperfections in our knowledge. An ontology for imperfect knowledge leads to a consistent classification of imperfections of data (i.e., data quality), and a formalizable description of the influence of data quality on decisions. If we want to deal with data quality with ontological methods, then reality and the information model stored in the GIS must be represented in the same model. This allows to use closed loops semantics to define "fitness for use" as leading to correct, executable decisions. The approach covers knowledge of physical reality as well as personal (subjective) and social constructions. It lists systematically influences leading to imperfections in data in logical succession.