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Data Quality Ontology: An Ontology for Imperfect Knowledge

  • Conference paper
Spatial Information Theory (COSIT 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4736))

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Abstract

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.

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Authors and Affiliations

  1. Institute for Geoinformation and Cartography, Vienna University of Technology Gusshausstrasse. 27-29, A-1040 Vienna, Austria

    Andrew U. Frank

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  1. Andrew U. Frank
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Stephan Winter Matt Duckham Lars Kulik Ben Kuipers

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Frank, A.U. (2007). Data Quality Ontology: An Ontology for Imperfect Knowledge. In: Winter, S., Duckham, M., Kulik, L., Kuipers, B. (eds) Spatial Information Theory. COSIT 2007. Lecture Notes in Computer Science, vol 4736. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74788-8_25

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  • DOI: https://doi.org/10.1007/978-3-540-74788-8_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74786-4

  • Online ISBN: 978-3-540-74788-8

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