Inconsistency-based Ranking of Knowledge Bases

Said Jabbour, Badran Raddaoui, Lakhdar Sais


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.


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Paper Citation

in Harvard Style

Jabbour S., Raddaoui B. and Sais L. (2015). Inconsistency-based Ranking of Knowledge Bases . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-074-1, pages 414-419. DOI: 10.5220/0005210704140419

in Bibtex Style

author={Said Jabbour and Badran Raddaoui and Lakhdar Sais},
title={Inconsistency-based Ranking of Knowledge Bases},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Inconsistency-based Ranking of Knowledge Bases
SN - 978-989-758-074-1
AU - Jabbour S.
AU - Raddaoui B.
AU - Sais L.
PY - 2015
SP - 414
EP - 419
DO - 10.5220/0005210704140419