Supporting Geo-Ontology Engineering Through Spatial Data Analytics

  • Gloria Re Calegari
  • Emanuela Carlino
  • Irene Celino
  • Diego Peroni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)

Abstract

Geo-ontologies are becoming first-class artifacts in spatial data management because of their ability to represent places and points of interest. Several general-purpose geo-ontologies are available and widely employed to describe spatial entities across the world. The cultural, contextual and geographic differences between locations, however, call for more specialized and spatially-customized geo-ontologies. In order to help ontology engineers in (re)engineering geo-ontologies, spatial data analytics can provide interesting insights on territorial characteristics, thus revealing peculiarities and diversities between places.

In this paper we propose a set of spatial analytics methods and tools to evaluate existing instances of a general-purpose geo-ontology within two distinct urban environments, in order to support ontology engineers in two tasks: (1) the identification of possible location-specific ontology restructuring activities, like specializations or extensions, and (2) the specification of new potential concepts to formalize neighborhood semantic models. We apply the proposed approach to datasets related to the cities of Milano and London extracted from LinkedGeoData, we present the experimental results and we discuss their value to assist geo-ontology engineering.

References

  1. 1.
    Frank, A.U.: Chapter 2: ontology for spatio-temporal databases. In: Sellis, T.K., et al. (eds.) Spatio-Temporal Databases. LNCS, vol. 2520, pp. 9–77. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Janowicz, K., Scheider, S., Pehle, T., Hart, G.: Geospatial semantics and linked spatiotemporal data-past, present, and future. Semant. Web J. 3(4), 321–332 (2012)Google Scholar
  3. 3.
    Stadler, C., Lehmann, J., Höffner, K., Auer, S.: Linkedgeodata: a core for a web of spatial open data. Semant. Web 3(4), 333–354 (2012)Google Scholar
  4. 4.
    Montello, D.R., Goodchild, M.F., Gottsegen, J., Fohl, P.: Where’s downtown?: behavioral methods for determining referents of vague spatial queries. Spat. Cogn. Comput. 3(2–3), 185–204 (2003)Google Scholar
  5. 5.
    Janowicz, K.: Observation-driven geo-ontology engineering. Trans. GIS 16(3), 351–374 (2012)CrossRefGoogle Scholar
  6. 6.
    Brodaric, B., Gahegan, M.: Experiments to examine the situated nature of geoscientific concepts. Spat. Cogn. Comput. 7(1), 61–95 (2007)Google Scholar
  7. 7.
    Goodchild, M.: Citizens as sensors: the world of volunteered geography. GeoJournal 69, 211–221 (2007)CrossRefGoogle Scholar
  8. 8.
    Brodaric, B.: Geo-pragmatics for the geospatial semantic web. Trans. GIS 11(3), 453–477 (2007)CrossRefGoogle Scholar
  9. 9.
    Tomko, M., Purves, R.S.: Venice, city of canals: characterizing regions through content classification. Trans. GIS 13(3), 295–314 (2009)CrossRefGoogle Scholar
  10. 10.
    Mooney, P., Corcoran, P.: The annotation process in OpenStreetMap. Trans. GIS 16(4), 561–579 (2012)CrossRefGoogle Scholar
  11. 11.
    Mülligann, C., Janowicz, K., Ye, M., Lee, W.-C.: Analyzing the spatial-semantic interaction of points of interest in volunteered geographic information. In: Egenhofer, M., Giudice, N., Moratz, R., Worboys, M. (eds.) COSIT 2011. LNCS, vol. 6899, pp. 350–370. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Suárez-Figueroa, M.C., Gómez-Pérez, A., Motta, E., Gangemi, A.: Ontology Engineering in a Networked World. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Wang, X., Hamilton, H.J.: Towards an ontology-based spatial clustering framework. In: Kégl, B., Lee, H.-H. (eds.) Canadian AI 2005. LNCS (LNAI), vol. 3501, pp. 205–216. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Noulas, A., Scellato, S., Mascolo, C., Pontil, M.: Exploiting semantic annotations for clustering geographic areas and users in location-based social networks. In: The Social Mobile Web (2011)Google Scholar
  15. 15.
    Rizzo, G., Falcone, G., Meo, R., Pensa, R.G., Troncy, R., Milicic, V.: Geographic summaries from crowdsourced data. In: Presutti, V., Blomqvist, E., Troncy, R., Sack, H., Papadakis, I., Tordai, A. (eds.) ESWC Satellite Events 2014. LNCS, vol. 8798, pp. 477–482. Springer, Heidelberg (2014)Google Scholar
  16. 16.
    Calegari, R.G., Carlino, E., Peroni, D., Celino, I.: Extracting urban land use from linked open geospatial data. ISPRS Int. J. Geo-Inf. 4(4), 2109–2130 (2015)CrossRefGoogle Scholar
  17. 17.
    Mooney, P., Corcoran, P., Winstanley, A.C.: Towards quality metrics for OpenStreetMap. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 514–517. ACM (2010)Google Scholar
  18. 18.
    Haklay, M., et al.: How good is volunteered geographical information? a comparative study of OpenStreetMap and ordnance survey datasets. Environ. Plann. B Plan. Des. 37(4), 682 (2010)CrossRefGoogle Scholar
  19. 19.
    Morisita, M.: Measuring of the dispersion of individuals and analysis of the distributional patterns. Mem. Fac. Sci. Kyushu Univ. Ser. E 2(21), 5–235 (1959)Google Scholar
  20. 20.
    Gittleman, J.L., Kot, M.: Adaptation: statistics and a null model for estimating phylogenetic effects. Syst. Biol. 39(3), 227–241 (1990)Google Scholar
  21. 21.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, pp. 226–231 (1996)Google Scholar
  22. 22.
    Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: ordering points to identify the clustering structure. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, pp. 49–60. ACM (1999)Google Scholar
  23. 23.
    Rokach, L., Maimon, O.: Clustering methods. In: Maimon, L., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  24. 24.
    Walker, A.R., Moody, M.P., Pham, B.L.: A spatial similarity ranking framework for spatial metadata retrieval (2006)Google Scholar
  25. 25.
    Gangemi, A., Presutti, V.: Ontology design patterns. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, pp. 221–243. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Gloria Re Calegari
    • 1
  • Emanuela Carlino
    • 1
  • Irene Celino
    • 1
  • Diego Peroni
    • 1
  1. 1.CEFRIEL – Politecnico of MilanoMilanoItaly

Personalised recommendations