A Case-Study of Ontology-Driven Semantic Mediation of Flower-Visiting Data from Heterogeneous Data-Stores in Three South African Natural History Collections

  • Willem Coetzer
  • Deshendran Moodley
  • Aurona Gerber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7955)


The domain complexity and structural- and semantic heterogeneity of biodiversity data, as well as idiosyncratic legacy data-creation processes, present significant integration and interoperability challenges. In this paper we describe a case-study of ontology-driven semantic mediation using records of flower-visiting insects from three natural history collections in South Africa. We establish a conceptual domain model for flower-visiting, expressed in an OWL ontology, and use it to semantically enrich the three data-stores. We show how this enrichment allows for the creation of an integrated flower-visiting dataset. We discuss how the ontology captures both implicit and explicit knowledge, and we show how the ontology can be used to identify and analyze high-level flower-visiting behaviour. We propose that a system that employs this ontology for semantic enrichment and semantic mediation may be used to automatically construct flower-visiting and pollination networks, the manually constructed equivalents of which are routinely used by domain scientists to analyze their data.


biodiversity information semantic mediation ontology plant-insect interactions pollination 


  1. 1.
    Johnson, N.F.: Biodiversity Informatics. Annual Review of Entomology 52, 421–438 (2007)CrossRefGoogle Scholar
  2. 2.
    Jones, M.B., Schildhauer, M.P., Reichman, O.J., Bowers, S.: The New Bioinformatics: Integrating Ecological Data From the Gene to the Biosphere. Annual Review of Ecology Evolution and Systematics 37, 519–544 (2006)CrossRefGoogle Scholar
  3. 3.
    Deans, A.R., Yoder, M.J., Balhoff, J.P.: Time to Change How We Describe Biodiversity. Trends in Ecology & Evolution 27, 78–84 (2011)CrossRefGoogle Scholar
  4. 4.
    Bisby, F.A.: The Quiet Revolution: Biodiversity Informatics and the Internet. Science 289, 2309–2312 (2000)CrossRefGoogle Scholar
  5. 5.
    Silvertown, J.: A New Dawn For Citizen Science. Trends in Ecology & Evolution 24, 467–471 (2009)CrossRefGoogle Scholar
  6. 6.
    Biodiversity Information Standards,
  7. 7.
    Wieczorek, J., Bloom, D., Guralnick, R., Blum, S., Döring, M., Giovanni, R., Robertson, T., Vieglais, D.: Darwin Core: An Evolving Community-Developed Biodiversity Data Standard. PLoS ONE 7, e29715 (2012)CrossRefGoogle Scholar
  8. 8.
    Kennedy, J., Hyam, R., Kukla, R., Paterson, T.: A Standard Data Model Representation for Taxonomic Information. Omics, A Journal of Integrative Biology 10, 220–230 (2006)CrossRefGoogle Scholar
  9. 9.
    Hyam, R., Kennedy, J.: Taxon Concept Schema – User Guide. Unpublished Report, 28 p. (2005)Google Scholar
  10. 10.
    Yoder, M.J., Mikó, I., Seltmann, K.C., Bertone, M.A., Deans, A.R.: A Gross Anatomy Ontology For Hymenoptera. PloS One 5, e15991 (2010)CrossRefGoogle Scholar
  11. 11.
    The Plant Ontology Consortium: The Plant OntologyTM Consortium and Plant Ontologies. Comparative and Functional Genomics 3, 137–142 (2002)Google Scholar
  12. 12.
    Webb, C., Baskauf, S.: Darwin-SW: Darwin Core Data for the Semantic WebGoogle Scholar
  13. 13.
    Michener, W.K., Brunt, J.W., Helly, J.J., Kirchner, T.B., Stafford, S.G.: Nongeospatial Metadata for the Ecological Sciences. Ecological Applications 7, 330–342 (1997)CrossRefGoogle Scholar
  14. 14.
    Johnson, J.C., Christian, R.R., Brunt, J.W., Hickman, C.R., Waide, R.B.: Evolution of Collaboration within the US Long Term Ecological Research Network. BioScience 60, 931–940 (2010)CrossRefGoogle Scholar
  15. 15.
    Williams, J.R., Martinez, N.D., Golbeck, J.: Ontologies for Ecoinformatics. Web Semantics: Science, Services and Agents on the World Wide Web 4, 237–276 (2006)CrossRefGoogle Scholar
  16. 16.
    Madin, J., Bowers, S., Schildhauer, M., Krivov, S., Pennington, D., Villa, F.: An Ontology for Describing and Synthesizing Ecological Observation Data. Ecological Informatics 2, 279–296 (2007)CrossRefGoogle Scholar
  17. 17.
    Michener, W.K., Beach, J.H., Jones, M.B., Ludäscher, B., Pennington, D.D., Pereira, R.S., Rajasekar, A., Schildhauer, M.: A Knowledge Environment for the Biodiversity and Ecological Sciences. Journal of Intelligent Information Systems 29, 111–126 (2007)CrossRefGoogle Scholar
  18. 18.
    Michener, W.K., Jones, M.B.: Ecoinformatics: Supporting Ecology as a Data-Intensive Science. Trends in Ecology & Evolution 27, 85–93 (2012)CrossRefGoogle Scholar
  19. 19.
    De Giovanni, R., Cartolano, E., Giannini, T., Saraiva, A., Pizzigatti, P.: Darwin Core Interaction Extension Concept List,
  20. 20.
    De Giovanni, R., Cartolano, E., Giannini, T., Saraiva, A., Pizzigatti, P.: Darwin Core Interaction Extension: Pollination Extension Concept List,
  21. 21.
    Coetzer, W., Gon, O., Hamer, M., Parker-Allie, F.: A New Era for Specimen Databases and Biodiversity Information Management in South Africa. Biodiversity Informatics 8, 1–11 (2012)Google Scholar
  22. 22.
    Raven, P.H., Evert, R.F., Eichhorn, S.E.: Biology of Plants. Worth Publishers, Inc., New York (1986)Google Scholar
  23. 23.
    Kevan, P.G., Baker, H.G.: Insects as Flower Visitors and Pollinators. Annual Review of Entomology 28, 407–453 (1983)CrossRefGoogle Scholar
  24. 24.
    Pauw, A.: Floral Syndromes Accurately Predict Pollination by a Specialized Oil-Collecting Bee (Rediviva peringueyi, Melittidae) in a Guild of South African Orchids (Coryciinae). American Journal of Botany 93, 917–926 (2006) ST – Floral syndromes accurately predict CrossRefGoogle Scholar
  25. 25.
    Whitehead, V.B., Steiner, K.E.: Oil-collecting Bees of the Winter Rainfall Area of South Africa. Annals of The South African Museum 108, 143–277 (2000)Google Scholar
  26. 26.
    Whitehead, V.B., Steiner, K.E., Eardley, C.D.: Oil Collecting Bees Mostly of the Summer Rainfall area of Southern Africa (Hymenoptera: Melittidae: Rediviva). Journal of the Kansas Entomological Society 81, 122–141 (2008)CrossRefGoogle Scholar
  27. 27.
    Horridge, M.: A Practical Guide To Building OWL Ontologies Using Protege 4 and CO-ODE Tools Edition 1.3 (2011)Google Scholar
  28. 28.
    Arp, R., Smith, B.: Function, Role, and Disposition in Basic Formal Ontology. Nature 2, 1–4 (2008)Google Scholar
  29. 29.
    Murphy, C.M., Breed, M.D.: Nectar and Resin Robbing in Stingless Bees. American Entomologist 36–44 (Spring 2008)Google Scholar
  30. 30.
    Hebert, P.D.N., Cywinska, A., Ball, S.L., DeWaard, J.R.: Biological identifications through DNA Barcodes. Proceedings of the Royal Society B: Biological Sciences 270, 313–321 (2003)CrossRefGoogle Scholar
  31. 31.
    Dupont, Y.L., Padron, B., Olesen, J.M., Petanidou, T.: Spatio-Temporal Variation in the Structure of Pollination Networks. Oikos 118, 1261–1269 (2009)CrossRefGoogle Scholar
  32. 32.
    Kaiser-Bunbury, C.N., Muff, S., Memmott, J., Müller, C.B., Caflisch, A.: The Robustness of Pollination Networks to the Loss of Species and Interactions: A Quantitative Approach Incorporating Pollinator Behaviour. Ecology Letters 13, 442–452 (2010)CrossRefGoogle Scholar
  33. 33.
    Valdovinos, F.S., Ramos-Jiliberto, R., Flores, J.D., Espinoza, C., López, G.: Structure and Dynamics of Pollination Networks: The Role of Alien Plants. Oikos 118, 1190–1200 (2009)CrossRefGoogle Scholar
  34. 34.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs (2003)Google Scholar
  35. 35.
    Moodley, D., Simonis, I., Tapamo, J.: An Architecture for Managing Knowledge and System Dynamism in the Worldwide Sensor Web. International Journal of Semantic Web and Information Systems: Special issue on Semantics-enhanced Sensor Networks. Internet of Things and Smart Devices 8, 64–88 (2012)CrossRefGoogle Scholar
  36. 36.
    Wiebes, J.T.: Co-evolution of Figs and Their Insect Pollinators. Annual Review of Ecology and Systematics 10, 1–12 (1979)CrossRefGoogle Scholar
  37. 37.
    Sala, A., Bergamaschi, S.: A Mediator Based Approach to Ontology Generation and Querying of Molecular and Phenotypic Cereals Data. International Journal of Metadata, Semantics and Ontologies 4(1/2), 85–92 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Willem Coetzer
    • 1
    • 2
  • Deshendran Moodley
    • 1
    • 2
  • Aurona Gerber
    • 1
    • 2
  1. 1.CAIR (Centre for Artificial Intelligence Research)University of KwaZulu-NatalDurbanSouth Africa
  2. 2.CSIR MerakaPretoriaSouth Africa

Personalised recommendations