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International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management

IC3K 2012: Knowledge Discovery, Knowledge Engineering and Knowledge Management pp 179–193Cite as

Towards Automatic Ontology Alignmentfor Enriching Sensor Data Analysis

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 415))

Abstract

In this work ontology alignment is used to align an ontology comprising high level knowledge to a structure representing the results of low-level sensor data classification. To resolve inherent uncertainties from the data driven classifier, an ontology about application domain is aligned to the classifier output and the result is recommendation system able to suggest a course of action that will resolve the uncertainty. This work is instantiated in a medical application domain where signals from an electronic nose are classified into different bacteria types. In case of misclassifications resulting from the data driven classifier, the alignment to an ontology representing traditional microbiology tests suggests a subset of tests most relevant to use. The result is a hybrid classification system (electronic nose and traditional testing) that automatically exploits domain knowledge in the identification process.

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Author information

Authors and Affiliations

  1. Applied Autonomous Sensor Systems, Dept of Technology, Örebro University, SE-701 82, Örebro, Sweden

    Marjan Alirezaie & Amy Loutfi

Authors
  1. Marjan Alirezaie
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  2. Amy Loutfi
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Editor information

Editors and Affiliations

  1. IST - Technical University of Lisbon, Av.Rovisco Pais, 1, 1049-001, Lisbon, Portugal

    Ana Fred

  2. Delft University of Technology, Mekelweg 4, 2628, Delft, CD, The Netherlands

    Jan L. G. Dietz

  3. Informatics Research Centre, Henley Business School, University of Reading, RG6 6UD, UK

    Kecheng Liu

  4. INSTICC and IPS, Estefanilha, Setúbal, Portugal

    Joaquim Filipe

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Alirezaie, M., Loutfi, A. (2013). Towards Automatic Ontology Alignmentfor Enriching Sensor Data Analysis. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2012. Communications in Computer and Information Science, vol 415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54105-6_12

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  • DOI: https://doi.org/10.1007/978-3-642-54105-6_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54104-9

  • Online ISBN: 978-3-642-54105-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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