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Fitness for use in geographic information: a dual ontology approach

Quality issues are important in geographic information (ranked #1 by users of GI products, both in request for information about data quality, and in claim about its use when available). The classical definition of quality as "fitness for use" cannot be implemented directly as a computable operator.

The proposition is to split the process into two distinct parts:
(1) to establish what is the user need for a particular problem, in terms of what features (with definition, categories, relations), with which quality (different quality elements required for each feature). This double set should be structured as a whole, and translated as a set of formal clauses called"problem ontology". A very simple version of this has been implemented with a table model (Excel-like) with computable cells;
(2) to translate the specifications of the different available data sets, into as many "product ontologies" (see the other REVIGIS result about their logical checking).

Then the fitness for use can be approximated this way:
- try to identify a cell of the problem ontology as a cell of one of the product ontologies, and, if acceptable, compare required quality with actual quality from the dataset;
- if correct, fitness is established;
- else: start negotiation: try to derive some data from what is available, using some derivation model (eg: a vege index from pixel colours, or interpolating missing values, etc.), and estimate a quality for this derived data, then back to previous steps. What is important to notice here: the problem ontology has been designed independently from the available data (this is not easy sometimes), in order to avoid any bias that anticipates the required quality from the available quality (impeding any actual fitness for use approximation).

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Universite de Provence
CMI, 39 rue Joliot-Curie
13453 Marseille
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