EFFICIENT TOLERANT PATTERN MATCHING WITH CONSTRAINT ABSTRACTIONS IN DESCRIPTION LOGIC

Carsten Elfers, Stefan Edelkamp, Otthein Herzog

Abstract

In this paper we consider efficiently matching logical constraint compositions called patterns by introducing a degree of satisfaction. The major advantage of our approach to other soft pattern matching methods is to exploit existing domain knowledge represented in Description Logic to handle imprecision in the data and to overcome the problem of an insufficient number of patterns. The matching is defined in a probabilistic framework to support post-processing with probabilistic models. Additionally, we propose an efficient complete algorithm for this kind of pattern matching, which reduces the number of inference calls to the reasoner. We analyze its worst-case complexity and compare it to a simple and to a theoretical optimal algorithm.

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Paper Citation


in Harvard Style

Elfers C., Edelkamp S. and Herzog O. (2012). EFFICIENT TOLERANT PATTERN MATCHING WITH CONSTRAINT ABSTRACTIONS IN DESCRIPTION LOGIC . In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-95-9, pages 256-261. DOI: 10.5220/0003720102560261


in Bibtex Style

@conference{icaart12,
author={Carsten Elfers and Stefan Edelkamp and Otthein Herzog},
title={EFFICIENT TOLERANT PATTERN MATCHING WITH CONSTRAINT ABSTRACTIONS IN DESCRIPTION LOGIC},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2012},
pages={256-261},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003720102560261},
isbn={978-989-8425-95-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - EFFICIENT TOLERANT PATTERN MATCHING WITH CONSTRAINT ABSTRACTIONS IN DESCRIPTION LOGIC
SN - 978-989-8425-95-9
AU - Elfers C.
AU - Edelkamp S.
AU - Herzog O.
PY - 2012
SP - 256
EP - 261
DO - 10.5220/0003720102560261