Acquisition and Representation of Knowledge for Atmospheric New Particle Formation

Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 413)


Sensors are used in environmental science to monitor an increasingly large multitude of properties of real world phenomena. An important scientific aim of such monitoring is more accurate and more complete understanding of phenomena, with respect to, e.g., their formation, development, or interactions. Properties and phenomena may be, for instance, mass or concentration and particulate matter or eutrophication, respectively. Typically, measurement data must undergo considerable processing in order to become useful to a scientific aim. We outline the architecture and implementation of an ontology-based environmental software system for the automated representation of knowledge for real world situations acquired from measurement data. We evaluate and discuss the system for the automated representation of knowledge for situations of atmospheric new particle formation. Such knowledge is acquired from measurement data for the particle size distribution of a polydisperse aerosol, as measured by a differential mobility particle sizer.


Knowledge representation new particle formation ontology situation theory machine learning 


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Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  1. 1.Environmental Informatics Group, Department of Environmental ScienceUniversity of Eastern FinlandKuopioFinland
  2. 2.Aerosol Physics Group, Department of Applied PhysicsUniversity of Eastern FinlandKuopioFinland
  3. 3.Finnish Meteorological InstituteHelsinkiFinland
  4. 4.Finnish Meteorological InstituteKuopioFinland

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