The Relevance of Measurement Data in Environmental Ontology Learning

  • Markus Stocker
  • Mauno Rönkkö
  • Ferdinando Villa
  • Mikko Kolehmainen
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 359)


Ontology has become increasingly important to software systems. The aim of ontology learning is to ease one of the major problems in ontology engineering, i.e. the cost of ontology construction. Much of the effort within the ontology learning community has focused on learning from text collections. However, environmental domains often deal with numerical measurement data and, therefore, rely on methods and tools for learning beyond text. We discuss this characteristic using two relations of an ontology for lakes. Specifically, we learn a threshold value from numerical measurement data for ontological rules that classify lakes according to nutrient status. We describe our methodology, highlight the cyclical interaction between data mining and ontologies, and note that the numerical value for lake nutrient status is specific to a spatial and temporal context. The use case suggests that learning from numerical measurement data is a research area relevant to environmental software systems.


Ontology learning rule-based reasoning environmental data 


  1. 1.
    Ashburner, M., Ball, C., Blake, J., Botstein, D., Butler, H., Cherry, J., Davis, A., Dolinski, K., Dwight, S., Eppig, J., Harris, M., Hill, D., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J., Richardson, J., Ringwald, M., Rubin, G., Sherlock, G.: Gene ontology: Tool for the unification of biology. Nature Genetics 25(1), 25–29 (2000)CrossRefGoogle Scholar
  2. 2.
    Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.C., Estreicher, A., Gasteiger, E., Martin, M., Michoud, K., O’Donovan, C., Phan, I., Pilbout, S., Schneider, M.: The Swiss-Prot Protein Knowledgebase and its supplement TrEMBL. Nucleic Acids Res. 31, 365–370 (2003)CrossRefGoogle Scholar
  3. 3.
    Carroll, J.J., Dickinson, I., Dollin, C., Reynolds, D., Seaborne, A., Wilkinson, K.: Jena: Implementing the Semantic Web Recommendations. Tech. Rep. HPL-2003-146, HP Laboratories, Bristol, UK (2003)Google Scholar
  4. 4.
    Gruber, T.: A translation approach to portable ontology specifications. Knowledge Acquisition 5(2), 199–220 (1993)CrossRefGoogle Scholar
  5. 5.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11 (2009)Google Scholar
  6. 6.
    Henson, C.A., Pschorr, J.K., Sheth, A.P., Thirunarayan, K.: SemSOS: Semantic Sensor Observation Service. In: Proc. of the 2009 International Symposium on Collaborative Technologies and Systems (CTS 2009), Baltimore, MD (May 2009)Google Scholar
  7. 7.
    Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American (2001)Google Scholar
  8. 8.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press (1967)Google Scholar
  9. 9.
    Manola, F., Miller, E.: RDF Primer. Tech. Rep. W3C Recommendation, W3C (2004)Google Scholar
  10. 10.
    Naumann, E.: Nagra synpunker angaende planktons okologi. Med sarskild hansyn till fytoplankton. Svensk Bot. Tidskr. 13, 129–158 (1919)Google Scholar
  11. 11.
    Nigro, H.O., Císaro, S.E.G., Xodo, D.H.: Data mining with ontologies: Implementations, findings, and frameworks. Information Science Reference (an imprint of IGI Global) (2008)Google Scholar
  12. 12.
    Prud’hommeaux, E., Seaborne, A.: SPARQL Query Language for RDF. Tech. Rep. W3C Recommendation, W3C (2008)Google Scholar
  13. 13.
    Shamsfard, M., Barforoush, A.: The state of the art in ontology learning: A framework for comparison. Knowledge Engineering Review 18(4), 293–316 (2003)CrossRefGoogle Scholar
  14. 14.
    Sydenham, P.H.: Handbook of Measurement Science: Volume 1 Theoretical Fundamentals. John Wiley & Sons, Chichester (1982)Google Scholar
  15. 15.
    Thienemann, A.: Physikalische und chemische Untersuchungen in den Maaren der Eifel. Verh. Naturh. Ver. preuss. Rheinl. u. Westfalens 71, 281–389 (1915)Google Scholar
  16. 16.
    Villa, F., Athanasiadis, I., Rizzoli, A.: Modelling with knowledge: A review of emerging semantic approaches to environmental modelling. Environmental Modelling and Software 24(5), 577–587 (2009)CrossRefGoogle Scholar
  17. 17.
    Williams, R., Martinez, N., Golbeck, J.: Ontologies for ecoinformatics. Web Semantics 4(4), 237–242 (2006)CrossRefGoogle Scholar
  18. 18.
    Zafar, A.: Taxonomy of lakes. Hydrobiologia 13(3), 287–299 (1959)CrossRefGoogle Scholar
  19. 19.
    Zhou, L.: Ontology learning: State of the art and open issues. Information Technology and Management 8(3), 241–252 (2007)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • Markus Stocker
    • 1
  • Mauno Rönkkö
    • 1
  • Ferdinando Villa
    • 2
  • Mikko Kolehmainen
    • 1
  1. 1.University of Eastern FinlandKuopioFinland
  2. 2.Basque Centre for Climate Change [BC3]BilbaoSpain

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