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Authors: Marjan Alirezaie and Amy Loutfi

Affiliation: Örebro University, Sweden

Keyword(s): Ontology Alignment, Decision Tree, Classification, Semantic Gap.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Collaboration and e-Services ; Data Engineering ; Decision Support Systems ; e-Business ; Enterprise Information Systems ; Information Integration ; Integration/Interoperability ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Ontology Matching and Alignment ; Symbolic Systems

Abstract: In this work we show how alignment techniques can be used to align an ontology to a decision tree representing the features used in classification of sensor signals. The sensor data represents time-series data from an electronic nose when measuring bacteria in blood samples. The objective is to provide from the classification of these signals an estimate of the type of bacteria present in the sample. As these classification are inherently uncertain, knowledge about standard laboratory tests are used together with the classification result in order to determine a subset of tests to conduct that should result in a fast identification of the bacteria. The information about the laboratory tests are contained in an ontology. The result from the alignment is new classifier where recommendations are given to a user (expert) based on the interpretation of the sensor data that is done automatically.

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Paper citation in several formats:
Alirezaie, M. and Loutfi, A. (2012). Ontology Alignment for Classification of Low Level Sensor Data. In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (IC3K 2012) - KEOD; ISBN 978-989-8565-30-3; ISSN 2184-3228, SciTePress, pages 89-97. DOI: 10.5220/0004137400890097

@conference{keod12,
author={Marjan Alirezaie. and Amy Loutfi.},
title={Ontology Alignment for Classification of Low Level Sensor Data},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development (IC3K 2012) - KEOD},
year={2012},
pages={89-97},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004137400890097},
isbn={978-989-8565-30-3},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development (IC3K 2012) - KEOD
TI - Ontology Alignment for Classification of Low Level Sensor Data
SN - 978-989-8565-30-3
IS - 2184-3228
AU - Alirezaie, M.
AU - Loutfi, A.
PY - 2012
SP - 89
EP - 97
DO - 10.5220/0004137400890097
PB - SciTePress

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