Quality of Geographic Information: Ontological approach and Artificial Intelligence Tools
This work addresses data quality issues for users in geographic information systems, but it is incremental as it builds on existing ontological frameworks without introducing a new paradigm.
The paper tackles the problem of translating data quality into 'fitness for use' information for user needs in geographic applications, using an ontological approach to illustrate solutions in data fusion cases, with the expectation that computationally tractable solutions will emerge in future AI tools.
The objective is to present one important aspect of the European IST-FET project "REV!GIS"1: the methodology which has been developed for the translation (interpretation) of the quality of the data into a "fitness for use" information, that we can confront to the user needs in its application. This methodology is based upon the notion of "ontologies" as a conceptual framework able to capture the explicit and implicit knowledge involved in the application. We do not address the general problem of formalizing such ontologies, instead, we rather try to illustrate this with three applications which are particular cases of the more general "data fusion" problem. In each application, we show how to deploy our methodology, by comparing several possible solutions, and we try to enlighten where are the quality issues, and what kind of solution to privilege, even at the expense of a highly complex computational approach. The expectation of the REV!GIS project is that computationally tractable solutions will be available among the next generation AI tools.