Ontology Patterns for Complex Activity Modelling

  • Georgios Meditskos
  • Stamatia Dasiopoulou
  • Vasiliki Efstathiou
  • Ioannis Kompatsiaris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8035)


In this paper we propose an activity patterns ontology to formally represent the relationships that drive the derivation of complex activities in terms of the activity types and temporal relations that need to be satisfied. The patterns implement the descriptions and situations (DnS) ontology pattern of DOLCE Ultra Lite, modelling activity classes of domain ontologies as instances. The aim is to allow the formal representation of activity interpretation models over activity classes that are generally characterized by intricate temporal associations, and where it is often the case that the aggregation of individual activities entails the existence of a new (composite) activity. Due to the expressive limitations of OWL, these semantics are often defined outside the ontology language, e.g. they are encapsulated in rules and they are tightly-coupled with implementation frameworks, hindering the interoperability and reuse of the underlying knowledge. As a proof of concept, we describe the implementation of the activity pattern semantics using dynamically generated SPARQL rules in terms of CONSTRUCT graph patterns.


ontologies patterns activity modelling SPARQL rules 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Allen, J.F.: Towards a general theory of action and time. Artif. Intell. 23(2), 123–154 (1984), zbMATHCrossRefGoogle Scholar
  2. 2.
    Anicic, D., Fodor, P., Rudolph, S., Stojanovic, N.: Ep-sparql: a unified language for event processing and stream reasoning. In: Proceedings of the 20th International Conference on World Wide Web, WWW 2011, pp. 635–644. ACM, New York (2011), Google Scholar
  3. 3.
    Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Grossniklaus, M.: Querying rdf streams with c-sparql. SIGMOD Rec. 39(1), 20–26 (2010)CrossRefGoogle Scholar
  4. 4.
    Beltran, V., Arabshian, K., Schulzrinne, H.: Ontology-based user-defined rules and context-aware service composition system. In: García-Castro, R., Fensel, D., Antoniou, G. (eds.) ESWC 2011 Workshops. LNCS, vol. 7117, pp. 139–155. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Bolles, A., Grawunder, M., Jacobi, J.: Streaming SPARQL - extending SPARQL to process data streams. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 448–462. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Chen, L., Nugent, C.D.: Ontology-based activity recognition in intelligent pervasive environments. International Journal of Web Information Systems 5(4), 410–430 (2009)CrossRefGoogle Scholar
  7. 7.
    Chen, L., Nugent, C.D., Wang, H.: A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. on Knowl. and Data Eng. 24(6), 961–974 (2012)CrossRefGoogle Scholar
  8. 8.
    Debattista, J., Scerri, S., Rivera, I., Handschuh, S.: Ontology-based rules for recommender systems. In: SeRSy, pp. 49–60 (2012)Google Scholar
  9. 9.
    DOLCE+DnS Ultralite (DUL) ontology,
  10. 10.
    Gangemi, A., Mika, P.: Understanding the semantic web through descriptions and situations. In: Meersman, R., Schmidt, D.C. (eds.) CoopIS/DOA/ODBASE 2003. LNCS, vol. 2888, pp. 689–706. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    van Hage, W.R., Malaisé, V., Segers, R., Hollink, L., Schreiber, G.: Design and use of the Simple Event Model (SEM). J. Web Sem. 9(2), 128–136 (2011)CrossRefGoogle Scholar
  12. 12.
    Knublauch, H., Hendler, J.A., Idehen, K.: SPIN - overview and motivation. W3C member submission, World Wide Web Consortium (February 2011)Google Scholar
  13. 13.
    Maria, K., Vasilis, E., Grigoris, A.: S-CRETA: Smart classroom real-time assistance. In: Novais, P., Hallenborg, K., Tapia, D.I., Rodríguez, J.M.C. (eds.) Ambient Intelligence - Software and Applications. AISC, vol. 153, pp. 67–74. Springer, Heidelberg (2012), CrossRefGoogle Scholar
  14. 14.
    May, W., Alferes, J.J., Amador, R.: An ontology- and resources-based approach to evolution and reactivity in the semantic web. In: Meersman, R. (ed.) OTM 2005, Part II. LNCS, vol. 3761, pp. 1553–1570. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Motik, B., Cuenca Grau, B., Sattler, U.: Structured objects in OWL: representation and reasoning. In: Proceedings of the 17th International Conference on World Wide Web (WWW 2008), pp. 555–564. ACM, New York (2008)CrossRefGoogle Scholar
  16. 16.
    Okeyo, G., Chen, L., Hui, W., Sterritt, R.: A hybrid ontological and temporal approach for composite activity modelling. In: Min, G., Wu, Y., Liu, L.C., Jin, X., Jarvis, S.A., Al-Dubai, A.Y. (eds.) TrustCom, pp. 1763–1770. IEEE Computer Society (2012)Google Scholar
  17. 17.
    Pan, F., Hobbs, J.R.: Time ontology in OWL. W3C working draft, W3C (September 2006),
  18. 18.
    Patkos, T., Chibani, A., Plexousakis, D., Amirat, Y.: A production rule-based framework for causal and epistemic reasoning. In: Bikakis, A., Giurca, A. (eds.) RuleML 2012. LNCS, vol. 7438, pp. 120–135. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    Patkos, T., Chrysakis, I., Bikakis, A., Plexousakis, D., Antoniou, G.: A reasoning framework for ambient intelligence. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds.) SETN 2010. LNCS, vol. 6040, pp. 213–222. Springer, Heidelberg (2010), CrossRefGoogle Scholar
  20. 20.
    Riboni, D., Pareschi, L., Radaelli, L., Bettini, C.: Is ontology-based activity recognition really effective? In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 427–431 (March 2011)Google Scholar
  21. 21.
    Riboni, D., Bettini, C.: Cosar: hybrid reasoning for context-aware activity recognition. Personal Ubiquitous Comput. 15(3), 271–289 (2011), CrossRefGoogle Scholar
  22. 22.
    Riboni, D., Bettini, C.: Owl 2 modeling and reasoning with complex human activities. Pervasive and Mobile Computing 7(3), 379–395 (2011)CrossRefGoogle Scholar
  23. 23.
    Scherp, A., Franz, T., Saathoff, C., Staab, S.: A core ontology on events for representing occurrences in the real world. Multimedia Tools and Applications 58(2), 293–331 (2012)CrossRefGoogle Scholar
  24. 24.
    Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: A practical OWL-DL reasoner. Web Semantics: Science, Services and Agents on the World Wide Web 5(2), 51–53 (2011)CrossRefGoogle Scholar
  25. 25.
    Stevenson, G., Knox, S., Dobson, S., Nixon, P.: Ontonym: a collection of upper ontologies for developing pervasive systems. In: Proceedings of the 1st Workshop on Context, Information and Ontologies, CIAO 2009, pp. 9:1–9:8. ACM, New York (2009)Google Scholar
  26. 26.
    Tappolet, J., Bernstein, A.: Applied Temporal RDF: Efficient Temporal Querying of RDF Data with SPARQL. In: Aroyo, L., et al. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 308–322. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  27. 27.
    Teymourian, K., Rohde, M., Paschke, A.: Fusion of background knowledge and streams of events. In: DEBS, pp. 302–313 (2012)Google Scholar
  28. 28.
    Wessel, M., Luther, M., Wagner, M.: The difference a day makes - recognizing important events in daily context logs. In: C&O:RR (2007)Google Scholar
  29. 29.
    Ye, J., Stevenson, G., Dobson, S.: A top-level ontology for smart environments. Pervasive and Mobile Computing 7(3), 359–378 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Georgios Meditskos
    • 1
  • Stamatia Dasiopoulou
    • 1
  • Vasiliki Efstathiou
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Centre of Research & TechnologyInformation Technologies InstituteHellas

Personalised recommendations