Re-Envisioning Data Description Using Peirce’s Pragmatics

  • Mark Gahegan
  • Benjamin Adams
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8728)


Given the growth in geographical data production, and the various mandates to make sharing of data a priority, there is a pressing need to facilitate the appropriate uptake and reuse of geographical data. However, describing the meaning and quality of data and thus finding data to fit a specific need remain as open problems, despite much research on these themes over many years. We have strong metadata standards for describing facts about data, and ontologies to describe semantic relationships among data, but these do not yet provide a viable basis on which to describe and share data reliably. We contend that one reason for this is the highly contextual and situated nature of geographic data, something that current models do not capture well — and yet they could. We show in this paper that a reconceptualization of geographical information in terms of Peirce’s Pragmatics (specifically firstness, secondness and thirdness) can provide the necessary modeling power for representing situations of data use and data production, and for recognizing that we do not all see and understand in the same way. This in turn provides additional dimensions by which intentions and purpose can be brought into the representation of geographical data. Doing so does not solve all problems related to sharing meaning, but it gives us more to work with. Practically speaking, enlarging the focus from data model descriptions to descriptions of the pragmatics of the data — community, task, and domain semantics — allows us to describe the how, who, and why of data. These pragmatics offer a mechanism to differentiate between the perceived meanings of data as seen by different users, specifically in our examples herein between producers and consumers. Formally, we propose a generative graphical model for geographic data production through pragmatic description spaces and a pragmatic data description relation. As a simple demonstration of viability, we also show how this model can be used to learn knowledge about the community, the tasks undertaken, and even domain categories, from text descriptions of data and use-cases that are currently available. We show that the knowledge we gain can be used to improve our ability to find fit-for-purpose data.


Bayesian Network Domain Ontology Geographic Data Geographic Information System Task Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adams, B., Gahegan, M.: Emerging data challenges for next-generation spatial data infrastructure. In: Winter, S., Rizos, C. (eds.) Research@Locate 2014, Canberra, Australia, April 7-9, pp. 118–129 (2014),
  2. 2.
    Artz, D., Gil, Y.: A survey of trust in computer science and the semantic web. Web Semantics: Science, Services and Agents on the World Wide Web 5(2), 58–71 (2007)CrossRefGoogle Scholar
  3. 3.
    Berry, B.J.: Approaches to regional analysis: a synthesis. Annals of the Association of American Geographers 54(1), 2–11 (1964)CrossRefGoogle Scholar
  4. 4.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  5. 5.
    Boisvert, E., Brodaric, B.: GroundWater Markup Language (GWML)-enabling groundwater data interoperability in spatial data infrastructures. Journal of Hydroinformatics 14(1), 93–107 (2012)CrossRefGoogle Scholar
  6. 6.
    Carmel, D., Zwerdling, N., Guy, I., Ofek-Koifman, S., Har’el, N., Ronen, I., Uziel, E., Yogev, S., Chernov, S.: Personalized social search based on the user’s social network. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 1227–1236. ACM, New York (2009)Google Scholar
  7. 7.
    Costello, M.J., Michener, W.K., Gahegan, M., Zhang, Z.Q., Bourne, P.E.: Biodiversity data should be published, cited, and peer reviewed. Trends in Ecology & Evolution 28(8), 454–461 (2013)CrossRefGoogle Scholar
  8. 8.
    Crompvoets, J., Bregt, A., Rajabifard, A., Williamson, I.: Assessing the worldwide developments of national spatial data clearinghouses. International Journal of Geographical Information Science 18(7), 665–689 (2004)CrossRefGoogle Scholar
  9. 9.
    Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29(2-3), 103–130 (1997)CrossRefzbMATHGoogle Scholar
  10. 10.
    Egenhofer, M.: Toward the semantic geospatial web. In: GIS 2002: Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems, pp. 1–4. ACM, New York (2002)Google Scholar
  11. 11.
    Egenhofer, M.J., Frank, A.: Object-oriented modeling for GIS. Journal of the Urban and Regional Information Systems Association 4(2), 3–19 (1992)Google Scholar
  12. 12.
    Fegraus, E.H., Andelman, S., Jones, M.B., Schildhauer, M.: Maximizing the value of ecological data with structured metadata: an introduction to ecological metadata language (EML) and principles for metadata creation. Bulletin of the Ecological Society of America 86(3), 158–168 (2005)CrossRefGoogle Scholar
  13. 13.
    Gahegan, M., Agrawal, R., Jaiswal, A., Luo, J., Soon, K.H.: A platform for visualizing and experimenting with measures of semantic similarity in ontologies and concept maps. Transactions in GIS 12(6), 713–732 (2008)CrossRefGoogle Scholar
  14. 14.
    Gahegan, M., Luo, J., Weaver, S.D., Pike, W., Banchuen, T.: Connecting GEON: Making sense of the myriad resources, researchers and concepts that comprise a geoscience cyberinfrastructure. Computers & Geosciences 35(4), 836–854 (2009)CrossRefGoogle Scholar
  15. 15.
    Gahegan, M., Pike, W.: A situated knowledge representation of geographical information. Transactions in GIS 10(5), 727–749 (2006)CrossRefGoogle Scholar
  16. 16.
    Gangemi, A., Guarino, N., Masolo, C., Oltramari, A., Schneider, L.: Sweetening ontologies with DOLCE. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, pp. 166–181. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  17. 17.
    Goodchild, M.F.: Geographic data modeling. Computers and Geosciences 18(4), 401–408 (1992)CrossRefGoogle Scholar
  18. 18.
    Goodchild, M.F.: Data models and data quality: problems and prospects. In: Environmental Modeling with GIS, pp. 94–104. Oxford University Press (1993)Google Scholar
  19. 19.
    Grira, J., Bédard, Y., Roche, S.: Spatial data uncertainty in the VGI world: Going from consumer to producer. Geomatica 64(1), 61–72 (2010)Google Scholar
  20. 20.
    Guarino, N.: Formal Ontology and Information Systems. In: Guarino, N. (ed.) International Conference on Formal Ontology in Information Systems (FOIS 1998), pp. 3–15. IOS Press, Trento (1998)Google Scholar
  21. 21.
    Heuvelink, G.B., Burrough, P.A., Stein, A.: Propagation of errors in spatial modelling with GIS. International Journal of Geographical Information System 3(4), 303–322 (1989)CrossRefGoogle Scholar
  22. 22.
    Hobbie, J.E., Carpenter, S.R., Grimm, N.B., Gosz, J.R., Seastedt, T.R.: The US long term ecological research program. BioScience 53(1), 21–32 (2003)CrossRefGoogle Scholar
  23. 23.
    Janowicz, K., Raubal, M., Kuhn, W.: The semantics of similarity in geographic information retrieval. Journal of Spatial Information Science (2), 29–57 (2011)Google Scholar
  24. 24.
    Janowicz, K., Schade, S., Bröring, A., Keßler, C., Maué, P., Stasch, C.: Semantic enablement for spatial data infrastructures. Transactions in GIS 14(2), 111–129 (2010)CrossRefGoogle Scholar
  25. 25.
    Keim, D.A.: Designing pixel-oriented visualization techniques: Theory and applications. IEEE Transactions on Visualization and Computer Graphics 6(1), 59–78 (2000)CrossRefGoogle Scholar
  26. 26.
    Kuhn, W.: Ontologies in support of activities in geographical space. International Journal of Geographical Information Science 15(7), 613–631 (2001)CrossRefGoogle Scholar
  27. 27.
    Langran, G., Chrisman, N.R.: A framework for temporal geographic information. Cartographica: The International Journal for Geographic Information and Geovisualization 25(3), 1–14 (1988)CrossRefGoogle Scholar
  28. 28.
    MacEachren, A.M., Robinson, A., Hopper, S., Gardner, S., Murray, R., Gahegan, M., Hetzler, E.: Visualizing geospatial information uncertainty: What we know and what we need to know. Cartography and Geographic Information Science 32(3), 139–160 (2005)CrossRefGoogle Scholar
  29. 29.
    MacKay, D.J.C.: Information Theory, Inference, and Learning Algorithms, 7.2nd edn. Cambridge University Press, Cambridge (2003)Google Scholar
  30. 30.
    Michener, W., Vieglais, D., Vision, T.J., Kunze, J., Cruse, P., Janée, G.: Dataone: Data observation network for earth - preserving data and enabling innovation in the biological and environmental sciences. D-Lib Magazine 17(1/2) (2011)Google Scholar
  31. 31.
    Pearl, J.: Causality: Models, reasoning and inference. Cambridge University Press, Cambridge (2000)Google Scholar
  32. 32.
    Peirce, C.S.: The Collected Papers of Charles Sanders Peirce. Harvard University Press (1931)Google Scholar
  33. 33.
    Peuquet, D.J.: Representations of space and time. Guilford Press (2002)Google Scholar
  34. 34.
    Pike, W., Gahegan, M.: Beyond ontologies: Toward situated representations of scientific knowledge. International Journal of Human-Computer Studies 65(7), 674–688 (2007)CrossRefGoogle Scholar
  35. 35.
    Raskin, R.G., Pan, M.J.: Knowledge representation in the semantic web for Earth and environmental terminology (SWEET). Computers & Geosciences 31(9), 1119–1125 (2005)CrossRefGoogle Scholar
  36. 36.
    Raubal, M.: Formalizing conceptual spaces. In: Varzi, A.C., Vieu, L. (eds.) Formal Ontology in Information Systems, Proceedings of the Third International Conference (FOIS 2004), pp. 153–164. IOS Press (2004)Google Scholar
  37. 37.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall (2010)Google Scholar
  38. 38.
    Saalfeld, A.: Conflation automated map compilation. International Journal of Geographical Information System 2(3), 217–228 (1988)CrossRefGoogle Scholar
  39. 39.
    Schwering, A.: Approaches to semantic similarity measurement for geo-spatial data: A survey. Transactions in GIS 12(1), 5–29 (2008)CrossRefGoogle Scholar
  40. 40.
    Sen, M., Duffy, T.: GeoSciML: development of a generic geoscience markup language. Computers & Geosciences 31(9), 1095–1103 (2005)CrossRefGoogle Scholar
  41. 41.
    Shi, W.: A generic statistical approach for modelling error of geometric features in GIS. International Journal of Geographical Information Science 12(2), 131–143 (1998)CrossRefGoogle Scholar
  42. 42.
    Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Transactions on Knowledge and Data Engineering 25(1), 158–176 (2013)CrossRefGoogle Scholar
  43. 43.
    Sinton, D.: The inherent structure of information as a constraint to analysis: Mapped thematic data as a case study. Harvard Papers on Geographic Information Systems 7, 1–17 (1978)Google Scholar
  44. 44.
    Sowa, J.F.: Syntax, semantics, and pragmatics of contexts. In: Ellis, G., Rich, W., Levinson, R., Sowa, J.F. (eds.) ICCS 1995. LNCS, vol. 954, pp. 1–15. Springer, Heidelberg (1995)Google Scholar
  45. 45.
    Worboys, M.F., Duckham, M.: GIS: a computing perspective. CRC Press (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mark Gahegan
    • 1
  • Benjamin Adams
    • 1
  1. 1.Centre for eResearchThe University of AucklandNew Zealand

Personalised recommendations