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.

本文介绍了 “REV!GIS” 项目的数据质量转化成 “适用性” 的方法，基于 “本体论” 的概念框架捕获应用中的显式和隐式知识，并通过三个特例应用程序演示了如何使用该方法来解决数据融合问题中的质量问题，并探讨了优先选择哪种解决方案，以便在下一代人工智能工具中提供计算可行的解决方案。