Advertisement

Towards Ontology Reasoning for Topological Cluster Labeling

  • Hatim ChahdiEmail author
  • Nistor Grozavu
  • Isabelle Mougenot
  • Younès Bennani
  • Laure Berti-Equille
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)

Abstract

In this paper, we present a new approach combining topological unsupervised learning with ontology based reasoning to achieve both: (i) automatic interpretation of clustering, and (ii) scaling ontology reasoning over large datasets. The interest of such approach holds on the use of expert knowledge to automate cluster labeling and gives them high level semantics that meets the user interest. The proposed approach is based on two steps. The first step performs a topographic unsupervised learning based on the SOM (Self-Organizing Maps) algorithm. The second step integrates expert knowledge in the map using ontology reasoning over the prototypes and provides an automatic interpretation of the clusters. We apply our approach to the real problem of satellite image classification. The experiments highlight the capacity of our approach to obtain a semantically labeled topographic map and the obtained results show very promising performances.

Keywords

Normalize Difference Vegetation Index Description Logic Unsupervised Learning Cluster Label Normalize Difference Water Index 
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.

Notes

Acknowledgment

This work was supported by the French Agence Nationale de la Recherche under Grant ANR-12-MONU-0001.

References

  1. 1.
    Baader, F.: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York (2003)zbMATHGoogle Scholar
  2. 2.
    DeFries, R., Townshend, J.: NDVI-derived land cover classifications at a global scale. Int. J. Remote Sens. 15(17), 3567–3586 (1994)CrossRefGoogle Scholar
  3. 3.
    Durand, N., Derivaux, S., Forestier, G., Wemmert, C., Gançarski, P., Boussaid, O., Puissant, A.: Ontology-based object recognition for remote sensing image interpretation. In: 19th IEEE International Conference on Tools with Artificial Intelligence, 2007. ICTAI 2007, vol. 1, pp. 472–479. IEEE (2007)Google Scholar
  4. 4.
    Forestier, G., Puissant, A., Wemmert, C., Gançarski, P.: Knowledge-based region labeling for remote sensing image interpretation. Comput. Environ. Urban Syst. 36(5), 470–480 (2012)CrossRefGoogle Scholar
  5. 5.
    Glimm, B., Horrocks, I., Motik, B., Stoilos, G., Wang, Z.: HermiT: an OWL 2 reasoner. J. Autom. Reason. 53(3), 245–269 (2014)CrossRefzbMATHGoogle Scholar
  6. 6.
    Group, W.O.W., et al.: OWL 2 web ontology language document overview (2009)Google Scholar
  7. 7.
    Hare, J.S., Lewis, P.H., Enser, P.G., Sandom, C.J.: Mind the gap: another look at the problem of the semantic gap in image retrieval. In: Electronic Imaging 2006, p. 607309. International Society for Optics and Photonics (2006)Google Scholar
  8. 8.
    Horrocks, I., Li, L., Turi, D., Bechhofer, S.: The instance store: Dl reasoning with large numbers of individuals. In: Proceedings of the 2004 Description Logic Workshop (DL 2004), pp. 31–40 (2004)Google Scholar
  9. 9.
    Horrocks, I., Sattler, U.: Ontology reasoning in the SHOQ (D) description logic. IJCAI 1, 199–204 (2001)Google Scholar
  10. 10.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)CrossRefGoogle Scholar
  11. 11.
    Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  12. 12.
    Li, Z., Li, J., Liao, Y., Wen, S., Tang, J.: Labeling clusters from both linguistic and statistical perspectives: a hybrid approach. Knowl. Based Syst. 76, 219–227 (2015)CrossRefGoogle Scholar
  13. 13.
    Lutz, C.: Description logics with concrete domains-a survey (2003)Google Scholar
  14. 14.
    McFeeters, S.: The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17(7), 1425–1432 (1996)CrossRefGoogle Scholar
  15. 15.
    Mei, Q., Shen, X., Zhai, C.: Automatic labeling of multinomial topic models. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 490–499. ACM (2007)Google Scholar
  16. 16.
    Popescul, A., Ungar, L.H.: Automatic labeling of document clusters. Unpublished manuscript (2000). http://citeseer.nj.nec.com/popescul00automatic.html
  17. 17.
    Rauber, A., Merkl, D.: Automatic labeling of self-organizing maps: making a treasure-map reveal its secrets. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 228–237. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  18. 18.
    Shadbolt, N., Berners-Lee, T., Hall, W.: The semantic web revisited. IEEE Intell. Syst. 21(3), 96–101 (2006). doi: 10.1109/MIS.2006.62 CrossRefGoogle Scholar
  19. 19.
    Sheeren, D., Quirin, A., Puissant, A., Gançarski, P., Weber, C.: Discovering rules with genetic algorithms to classify urban remotely sensed data. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2006), pp. 3919–3922 (2006)Google Scholar
  20. 20.
    Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: a practical OWL-DL reasoner. Web Semant. Sci. Serv. Agents. World Wide Web 5(2), 51–53 (2007)CrossRefGoogle Scholar
  21. 21.
    Treeratpituk, P., Callan, J.: Automatically labeling hierarchical clusters. In: Proceedings of the 2006 International Conference on Digital Government Research, pp. 167–176. Digital Government Society of North America (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Hatim Chahdi
    • 1
    • 2
    Email author
  • Nistor Grozavu
    • 2
  • Isabelle Mougenot
    • 1
  • Younès Bennani
    • 2
  • Laure Berti-Equille
    • 1
    • 3
  1. 1.Espace-Dev UMR 228, IRD - Université de MontpellierMontpellierFrance
  2. 2.LIPN CNRS UMR 7030, CNRS - Université Paris 13VilletaneuseFrance
  3. 3.Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar

Personalised recommendations