@inproceedings{0499448d9a30464cbd57445176b352f2,
title = "Inconsistency-based ranking of knowledge bases",
abstract = "Inconsistencies are a usually undesirable feature of many kinds of data and knowledge. Measuring inconsistency is potentially useful to determine which parts of the data or of the knowledge base are conflicting. Several measures have been proposed to quantify such inconsistencies. However, one of the main problems lies in the difficulty to compare their underlying quality. Indeed, a highly inconsistent knowledge base with respect to a given inconsistency measure can be considered less inconsistent using another one. In this paper, we propose a new framework allowing us to partition a set of knowledge bases as a sequence of subsets according to a set of inconsistency measures, where the first element of the partition corresponds to the most inconsistent one. Then we discuss how finer ranking between knowledge bases can be derived from an original combination of existing measures. Finally, we extend our framework to provide some inconsistency measures obtained by combining existing ones.",
keywords = "Knowledge representation, Measuring inconsistency, Pareto optimality",
author = "Said Jabbour and Badran Raddaoui and Lakhdar Sais",
year = "2015",
month = jan,
day = "1",
doi = "10.5220/0005210704140419",
language = "English",
series = "ICAART 2015 - 7th International Conference on Agents and Artificial Intelligence, Proceedings",
publisher = "SciTePress",
pages = "414--419",
editor = "Stephane Loiseau and Joaquim Filipe and Joaquim Filipe and Beatrice Duval and \{van den Herik\}, Jaap",
booktitle = "ICAART 2015 - 7th International Conference on Agents and Artificial Intelligence, Proceedings",
note = "7th International Conference on Agents and Artificial Intelligence, ICAART 2015 ; Conference date: 10-01-2015 Through 12-01-2015",
}