Quality of Geographic Information: Ontological approach and Artificial Intelligence Tools

TL;DR

This paper presents an ontological methodology for translating geographic data quality into 'fitness for use' metrics, using AI tools within the REV!GIS project to address data fusion and quality issues.

Contribution

It introduces a novel ontological framework for assessing geographic data quality and demonstrates its application through three specific data fusion cases.

Findings

Identifies key quality issues in geographic data

Shows how to deploy ontologies for data fusion

Highlights potential AI tools for computationally tractable solutions

Abstract

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.