Automatic Annotation of Sensor Data Streams using Abductive Reasoning

Marjan Alirezaie, Amy Loutfi

Abstract

Fast growing structured knowledge in machine processable formats such as RDF/OWL provides the opportunity of having automatic annotation for stream data in order to extract meaningful information. In this work, we propose a system architecture to model the process of stream data annotation in an automatized fashion using public repositories of knowledge. We employ abductive reasoning which is capable of retrieving the best explanations for observations given incomplete knowledge. In order to evaluate the effectiveness of the framework, we use multivariate data coming from medical sensors observing a patient in ICU (Intensive Care Unit) suffering from several diseases as the ground truth against which the eventual explanations (annotations) of the reasoner are compared.

References

  1. Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., and Steggles, P. (1999). Towards a better understanding of context and context-awareness. In Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing, HUC 7899, pages 304-307, London, UK, UK. Springer-Verlag.
  2. Adamusiak, T., Burdett, T., Kurbatova, N., and et al (2011). Ontocat - simple ontology search and integration in java, r and rest/javascript. BMC Bioinformatics, 12:218.
  3. Alirezaie, M. and Loutfi, A. (2012). Ontology alignment for classification of low level sensor data. In KEOD, pages 89-97.
  4. Bache, K. and Lichman, M. (2013). Uci-machine learning repository. Irvine, CA: University of California, School of Information and Computer Science.
  5. Belkacem, C., Shengrui, W., and Hélène, P. (2011). Activity recognition in smart environments: an information retrieval problem. In Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics, ICOST'11, pages 33-40, Berlin, Heidelberg. Springer-Verlag.
  6. Compton, M. and et al (2012). The ssn ontology of the w3c semantic sensor network incubator group. Web Semantics: Science, Services and Agents on the World Wide Web.
  7. Coradeschi, S., Loutfi, A., and Wrede, B. (2013). A short review of symbol grounding in robotic and intelligent systems. KI - Knstliche Intelligenz, 27(2):129-136.
  8. Daoutis, M., Coradeschi, S., and Loutfi, A. (2009). Grounding commonsense knowledge in intelligent systems. JAISE, 1(4):311-321.
  9. Graves, G. R. and Rhodes, P. G. (1984). Tachycardia as a sign of early onset neonatal sepsis. Pediatr Infect Dis, 3(5):404-6.
  10. Henson, C., Thirunarayan, K., and Sheth, A. P. (2011a). An ontological approach to focusing attention and enhancing machine perception on the web. Appl. Ontol., 6(4):345-376.
  11. Henson, C. A., Sheth, A. P., and Thirunarayan, K. (2012). Semantic perception: Converting sensory observations to abstractions. IEEE Internet Computing, 16(2):26-34.
  12. Henson, C. A., Thirunarayan, K., Sheth, A. P., and , P. H. (2011b). Representation of parsimonious covering theory in owl-dl. In OWLED.
  13. Joshi, R. and Sanderson, A. C. (1999). Multisensor fusion : a minimal representation framework. Series in Intelligent Control and Intelligent Automation. World Scientific, Singapore, London, Hong Kong.
  14. Loutfi, A., Coradeschi, S., and Saffiotti, A. (2005). Maintaining coherent perceptual information using anchoring. In Proc. of the 19th IJCAI Conf., Edinburgh, UK. Online at http://www.aass.oru.se/˜ali/.
  15. Marneffe, M., MacCartney, B., and Manning, C. D. (2006). Generating typed dependency parses from phrase structure parses. Technical report, LREC.
  16. Perera, S., Henson, C. A., Thirunarayan, K., and Sheth, A. P. (2012). Data driven knowledge acquisition method for domain knowledge enrichment in the healthcare. In BIBM, pages 1-8.
  17. Reggia, J. A. and Peng, Y. (1986). Modeling diagnostic reasoning: A summary of parsimonious covering theory. Comput Methods Programs Biomed, 25(2):125-34.
  18. Salvadores, M., Alexander, P. R., Musen, M. A., and Noy, N. F. (2012). Bioportal as a dataset of linked biomedical ontologies and terminologies in rdf. SWJ.
  19. Thirunarayan, K., Henson, C. A., and Sheth, A. P. (2009). Situation awareness via abductive reasoning from semantic sensor data: A preliminary report. In CTS, pages 111-118.
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Paper Citation


in Harvard Style

Alirezaie M. and Loutfi A. (2013). Automatic Annotation of Sensor Data Streams using Abductive Reasoning . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2013) ISBN 978-989-8565-81-5, pages 345-354. DOI: 10.5220/0004623403450354


in Bibtex Style

@conference{keod13,
author={Marjan Alirezaie and Amy Loutfi},
title={Automatic Annotation of Sensor Data Streams using Abductive Reasoning},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2013)},
year={2013},
pages={345-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004623403450354},
isbn={978-989-8565-81-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2013)
TI - Automatic Annotation of Sensor Data Streams using Abductive Reasoning
SN - 978-989-8565-81-5
AU - Alirezaie M.
AU - Loutfi A.
PY - 2013
SP - 345
EP - 354
DO - 10.5220/0004623403450354