Semi-automatic Composition of Ontologies for ASKALON Grid Workflows

  • Muhammad Junaid Malik
  • Thomas Fahringer
  • Radu Prodan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7155)

Abstract

Automatic workflow composition with the help of ontologies has been addressed by numerous researchers in the past. While ontologies are very useful for automatic and semiautomatic workflow composition, ontology creation itself remains a very important and complex task.

In this paper we present a novel tool to synthesize ontologies for the Abstract Grid Workflow Language (AGWL) which has been used for years to successfully create Grid workflow applications at a high level of abstraction. In order to semi-automatically generate ontologies we use an AGWL Ontology (AGWO - an ontological description of the AGWL language), structural information of one or several input workflows of a given application domain, and semantic enrichment of the structural information with the help of the user. Experiments based on two separate application domains (movie rendering and meteorology) will be shown that demonstrate the effectiveness of our approach by semi-automatically generating ontologies which are then used to automatically create workflow applications.

Keywords

Domain Ontology Ontology Alignment Semantic Enrichment Ontology Evaluation Activity Convert 
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.

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References

  1. 1.
    Bohring, H., Auer, S.: Mapping xml to owl ontologies. In: Jantke, K.P., Fhnrich, K.-P., Wittig, W.S. (eds.) Leipziger Informatik-Tage. LNI, vol. 72, pp. 147–156. GI (2005)Google Scholar
  2. 2.
    Brewster, C., Alani, H., Dasmahapatra, S., Wilks, Y.: Data driven ontology evaluation. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC), Lisbon, Portugal (2004)Google Scholar
  3. 3.
    Callahan, S.P., Freire, J., Santos, E., Scheidegger, C.E., Silva, C.T., Vo, H.T.: Vistrails: visualization meets data management. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, SIGMOD 2006, pp. 745–747. ACM, New York (2006)CrossRefGoogle Scholar
  4. 4.
    Castano, S., Ferrar, A., Montanelli, S., Hess, G.N., Bruno, S.: State of the art on ontology coorination and matching. deliverable 4.4 version 1.0 final (2007)Google Scholar
  5. 5.
    Deelman, E., Singh, G., Su, M.H., Blythe, J., Gil, A., Kesselman, C., Mehta, G., Vahi, K., Berriman, G.B., Good, J., Laity, A., Jacob, J.C., Katz, D.S.: Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Scientific Programming Journal 13, 219–237 (2005)Google Scholar
  6. 6.
    Fahringer, T., Jugravu, A., Pllana, S., Prodan, R., Seragiotto, C., Truong, H.L.: ASKALON: a tool set for cluster and Grid computing. Concurrency - Practice and Experience 17(2-4), 143–169 (2005)CrossRefGoogle Scholar
  7. 7.
    Foster, I., Kesselman, C. (eds.): The grid: blueprint for a new computing infrastructure. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  8. 8.
  9. 9.
    Web ontology language, http://www.w3.org/2004/OWL/
  10. 10.
    Euzenat, J., Le Bach, T., Barrasa, J., Bouquet, P., De Bo, J., Dieng, R., Ehrig, M., Hauswirth, M., Jarrar, M., Lara, R., Maynard, D., Napoli, A., Stamou, G., Stuckenschmidt, H., Shvaiko, P., Tessaris, S., Van Acker, S., Zaihrayeu, I.: State of the art on ontology alignment. knowledge web deliverable d2.2.3 (2004)Google Scholar
  11. 11.
    Junaid, M., Berger, M., Vitvar, T., Plankensteiner, K., Fahringer, T.: Workflow composition through design suggestions using design-time provenance information. In: 5th IEEE International Conference on E-Science Workshops, pp. 110–117 (December 2009)Google Scholar
  12. 12.
    Lozano-Tello, A., Gómez-Pérez, A.: Ontometric: A method to choose the appropriate ontology. Journal of Database Management 15(2) (April-June 2004)Google Scholar
  13. 13.
    Maedche, A., Staab, S.: Measuring Similarity between Ontologies. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, pp. 251–263. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Automatic Ontology Generation: State of the Art. University of Versailles Technical report. Ivan bedini and benjamin nguyen (2007)Google Scholar
  15. 15.
    Porzel, R., Malaka, R.: A Task-based Approach for ontology Evaluation. In: Workshop on Ontology Learning and Population, ECAI 2004 (2004)Google Scholar
  16. 16.
    Qin, J., Fahringer, T.: A novel domain oriented approach for scientific grid workflow composition. In: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, SC 2008, pp. 21:1–21:12. IEEE Press, Piscataway (2008)Google Scholar
  17. 17.
    Slota, R., Zieba, J., Kryza, B., Kitowski, J.: Knowledge evolution supporting automatic workflow composition. In: e-Science, p. 37. IEEE Computer Society (2006)Google Scholar
  18. 18.
    Sure, Y., Studer, R., Fensel, C.D., Lebensversicherungsund, S., Reimer, C.U.: On-to-knowledge methodology - final version (2002)Google Scholar
  19. 19.
    Taylor, I., Deelman, E., Gannon, D., Shields, M.: Workflows for e-science. XXII, 530 p. 181 illus., Hardcover (2007)Google Scholar
  20. 20.
    Taylor, I., Wang, I., Shields, M., Majithia, S.: Distributed computing with triana on the grid: Research articles. Concurr. Comput.: Pract. Exper. 17, 1197–1214 (2005)CrossRefGoogle Scholar
  21. 21.
    Vrandecic, D., Pinto, H.S., Sure, Y., Tempich, C.: The diligent knowledge processes. Journal of Knowledge Management 9(5), 85–96 (2005)CrossRefGoogle Scholar
  22. 22.
    Wächter, T., Schroeder, M.: Semi-automated ontology generation within obo-edit. Bioinformatics 26, i88–i96 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Muhammad Junaid Malik
    • 1
  • Thomas Fahringer
    • 1
  • Radu Prodan
    • 1
  1. 1.Institute of Computer ScienceUniversity of InnsbruckInnsbruckAustria

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