General information spaces: measuring inconsistency, rationality postulates, and complexity

Abstract

AI systems often need to deal with inconsistent information. For this reason, since the early 2000s, some AI researchers have developed ways to measure the amount of inconsistency in a knowledge base. By now there is a substantial amount of research about various aspects of inconsistency measuring. The problem is that most of this work applies only to knowledge bases formulated as sets of formulas in propositional logic. Hence this work is not really applicable to the way that information is actually stored. The purpose of this paper is to extend inconsistency measuring to real world information. We first define the concept of general information space which encompasses various types of databases and scenarios in AI systems. Then, we show how to transform any general information space to an inconsistency equivalent propositional knowledge base, and finally apply propositional inconsistency measures to find the inconsistency of the general information space. Our method allows for the direct comparison of the inconsistency of different information spaces, even though the data is presented in different ways. We demonstrate the transformation on four general information spaces: a relational database, a graph database, a spatio-temporal database, and a Blocks world scenario, where we apply several inconsistency measures after performing the transformation. Then we review so-called rationality postulates that have been developed for propositional knowledge bases as a way to judge the intuitive properties of these measures. We show that although general information spaces may be nonmonotonic, there is a way to transform the postulates so they can be applied to general information spaces and we show which of the measures satisfy which of the postulates. Finally, we discuss the complexity of inconsistency measures for general information spaces.

Guest Editorial: Reasoning With Inconsistent, Incomplete, and Uncertain Knowledge

“…6,7 Approaches that are complementary to those mentioned previously aim at quantifying the amount of inconsistent and uncertain information in knowledge bases. 8,9 Quantifying and monitoring the amount of inconsistency helps get information on the health status of data, whose quality is more and more important nowadays. Indeed, having information on the quality of data used in machine learning and datadriven approaches is crucial, as poor-quality data can have serious adverse consequences on the quality of decisions made using AI systems.…”