Ontology Alignment for Classification of Low Level Sensor Data

Marjan Alirezaie, Amy Loutfi

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.

References

  1. ARUP (2006). A national clinical and anatomic pathology reference laboratory. www.aruplab.com.
  2. Bishop, C. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, 1 edition.
  3. Bouza, A., Reif, G., Bernstein, A., and Gall, H. (2008). Semtree: Ontology-based decision tree algorithm for recommender systems. In International Semantic Web Conference (Posters & Demos).
  4. Chen, Y. (2010). Development of a method for ontologybased empirical knowledge representation and reasoning. Decision Support Systems, 50(1):1-20.
  5. Ehrig, M. (2007). Ontology Alignment: Bridging the Semantic Gap, volume 4 of Semantic Web And Beyond Computing for Human Experience. Springer.
  6. Euzenat, J. and Shvaiko, P. (2007). Ontology matching. Springer-Verlag, Heidelberg (DE).
  7. Hlaoui, A. (2002). A new algorithm for inexact graph matching. Object recognition supported by user interaction for service robots, 4(c):180-183.
  8. Jaro, M. (1989). Advances in record-linkage methodology as applied to matching the 1985 census of tampa, florida. Journal of the American Statistical Society. 3Ontology Alignment Evaluation Initiative (http://
  9. Joshi, R. and Sanderson, A. (1999). Multisensor Fusion: A Minimal Representation Framework. Series in Intelligent Control and Intelligent Automation. World Scientific.
  10. Melchert, J., Coradeschi, S., and Loutfi, A. (2007). Knowledge representation and reasoning for perceptual anchoring. Tools with Artificial Intelligence.
  11. Pearce, T., Schiffman, S., Nagle, H., and Gardner, J. (2003). Handbook of machine olfaction: electronic nose technology. Wiley-VCH.
  12. Quinlan, R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, C.
  13. Seltmann, G. and Holst, O. (2002). The Bacterial Cell Wall. Springer-Verlag.
  14. Trincavelli, M., Coradeschi, S., Lout, A., Sderquist, B., and Thunberg, P. (2010). Direct identication of bacteria in blood culture samples using an electronic nose. IEEE Trans Biomedical Engineering.
  15. Yuguang, N., Gaowei, Y., Gang, X., Zehua, C., and Keming, X. (2008). Multi-sensor fusion using knowledgebased mind evolutionary algorithm. Convergence and Hybrid Information Technology.
  16. Zhang, J., Silvescu, A., and Honavar, V. (2002). Ontologydriven induction of decision trees at multiple levels of abstraction. In In Proceedings of Symposium on Abstraction, Reformulation, and Approximation 2002. Springer-Verlag.
Download


Paper Citation


in Harvard Style

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 - Volume 1: KEOD, (IC3K 2012) ISBN 978-989-8565-30-3, pages 89-97. DOI: 10.5220/0004137400890097


in Bibtex Style

@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 - Volume 1: KEOD, (IC3K 2012)},
year={2012},
pages={89-97},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004137400890097},
isbn={978-989-8565-30-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2012)
TI - Ontology Alignment for Classification of Low Level Sensor Data
SN - 978-989-8565-30-3
AU - Alirezaie M.
AU - Loutfi A.
PY - 2012
SP - 89
EP - 97
DO - 10.5220/0004137400890097