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2013 Volume 28
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RESEARCH ARTICLE   Open Access    

The PORSCE II framework: using AI planning for automated Semantic Web service composition

  • 1.

    Department of Informatics and Telematics

  • 2.

    Department of Informatics

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  • Abstract: This paper presents PORSCE II, an integrated system that performs automatic Semantic Web service composition exploiting artificial intelligence (AI) techniques, specifically planning. Essential steps in achieving Web service composition include the translation of the Web service composition problem into a solver-ready planning domain and problem, followed by the acquisition of solutions, and the translation of the solutions back to Web service terms. The solutions to the problem, that is, the descriptions of the desired composite service, are obtained by means of external domain-independent planning systems, they are visualized and finally evaluated. Throughout the entire process, the system exploits semantic information extracted from the semantic descriptions of the available Web services and the corresponding ontologies, in order to perform composition under semantic awareness and relaxation.
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  • CMU. (2005). OWL-S API. Retrieved April, 17, 2010, from http://www.daml.ri.cmu.edu/owlsapi/.

    Google Scholar

    Dustdar S., Schreiner W.2005. A survey on web services composition. International Journal of Web and Grid Services1(1), 1–30.

    Google Scholar

    Fikes R. E., Nilsson N. J.1971. STRIPS: a new approach to the application of theorem proving to problem solving. In IJCAI'71: Proceedings of the 2nd InternationalJoint Conference on Artificial Intelligence, San Francisco, CA, USA, 608–620. Morgan Kaufmann Publishers Inc.

    Google Scholar

    Fox M., Long D.2002. PDDL+: Modelling continuous time-dependent effects. In Proceedings of the 3rdInternational NASA Workshop on Planning and Scheduling for Space, Houston, USA.

    Google Scholar

    Gerevini A., Long D.2005. Plan Constraints and Preferences in PDDL3. Technical report, Department of Electronics for Automation, University of Brescia, Italy.

    Google Scholar

    Gerevini A., Saetti A., Serina I., Toninelli P.2004. LPG-td: a fully automated planner for PDDL2.2 domains. In Proceedings of the 14th International Conference on Automated Planning and Scheduling (ICAPS-04) International Planning Competition, Whistler, British Columbia, Canada.

    Google Scholar

    Ghallab M., Howe A., Knoblock C., McDermott D., Ram A., Veloso M., Weld D., Wilkins D.1998. PDDL – the planning domain definition language. Technical report, Yale University, New Haven, CT.

    Google Scholar

    Hatzi O., Meditskos G., Vrakas D., Bassiliades N., Anagnostopoulos D., Vlahavas I.2008. A synergy of planning and ontology concept ranking for semantic web service composition. In IBERAMIA '08: Proceedings of the 11th Ibero-American Conference on AI, Geffner, H., Prada, R., Machado Alexandre, I. & David, N. (eds). Berlin, Heidelberg, 42–51. Springer-Verlag.

    Google Scholar

    Hatzi O., Meditskos G., Vrakas D., Bassiliades N., Anagnostopoulos D., Vlahavas I.2009. PORSCE II: using planning for semantic web service composition. In Proceedings of 3rd International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS'09), in conjunction with the International Conference on Automated Planning and Scheduling (ICAPS-09), Bartak, R., Frattini, S. & McCluskey, L. (eds), 38–45.

    Google Scholar

    Hatzi O., Vrakas D., Bassiliades N., Anagnostopoulos D., Vlahavas I.2007. VLEPPO: A visual language for problem representation. In PlanSIG 2007: The 26th workshop of the UK Planning and Scheduling Special Interest Group, Bartak, R. (ed.), 60–66.

    Google Scholar

    International Planning Competition (IPC).2004. Retrieved October, 27, 2010, from http://www.tzi.de/edelkamp/ipc-4/.

    Google Scholar

    Java Graphplan (JPlan). (2009). Java Graphplan Implementation. Retrieved October, 27, 2010, from http://sourceforge.net/projects/jplan.

    Google Scholar

    Klusch M., Gerber A.2005. Semantic web service composition planning with OWLS-XPlan. In Proceedings of the 1st International AAAI Fall Symposium on Agents and the Semantic Web, Arlington VA, USA, 55–62.

    Google Scholar

    Maedche A., Zacharias V.2002. Clustering ontology-based metadata in the semantic web. In PKDD '02: Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, Elomaa, T., Mannila, H. & Toivonen, H. (eds). London, UK, 348–360. Springer-Verlag.

    Google Scholar

    McDermott D.2002. Estimated-regression planning for interactions with web services. In Proceedings of the 6th International Conference on Artificial Intelligence PlanningSystems, Ghallab, M., Hertzberg, J. & Traverso, P. (eds). Toulouse, France, 204–211. AAAI Press.

    Google Scholar

    Mcilraith S., Son T. C.2002. Adapting GOLOG for composition of semantic web services. In Proceedings of the 8th International Conference on Knowledge Representation and Reasoning, Toulouse, France, 482–493.

    Google Scholar

    OWL (Web Ontology Language). (2004). Retrieved October, 27, 2010, from http://www.w3.org/TR/owl-ref/.

    Google Scholar

    OWL-S. (2004). 1.1. Retrieved October, 27, 2010, from http://www.daml.org/services/owl-s/1.1/.

    Google Scholar

    OWLS-TC. (2005). SemWebCentral. Retrieved October, 27, 2010, from http://projects.semwebcentral.org/projects/owls-tc/.

    Google Scholar

    Paolucci M., Ankolekar A., Srinivasan N., Sycara K.2003. The DAML-S virtual machine. In The SemanticWeb—ISWC 2003, Lecture Notes in Computer Science 2870, 290–305. Springer.

    Google Scholar

    Pistore M., Marconi A., Bertoli P., Traverso P.2005. Automated composition of web services by planning at the knowledge level. In Proceedings of 19th International Joint Conferences on Artificial Intelligence, San Francisco, CA, USA, 1252–1259.

    Google Scholar

    Ponnekanti S. R., Fox A.2002. SWORD: a developer toolkit for web service composition. In Proceedings of the 11th International WWW Conference (WWW2002), Lassner, D., De Roure, D. & Iyengar, A. (eds). Honolulu, HI, USA, 83–107. Elsevier.

    Google Scholar

    Rao J., Su X.2004. A survey of automated web service composition methods. In Proceedings of the 1st International Workshop on Semantic Web Services and Web Process Composition, SWSWPC, San Diego, CA, USA, 43–54.

    Google Scholar

    Rule Markup Language (RuleML). (2001). The Rule Markup Initiative. Retrieved October, 27, 2010, from http://ruleml.org/.

    Google Scholar

    Semantic Annotations for WSDL (SAWSDL). (2007). Retrieved October, 27, 2010, from http://www.w3.org/2002/ws/sawsdl/.

    Google Scholar

    Sirin E., Parsia B., Wu D., Hendler J., Nau D.2004. HTN planning for web service composition using SHOP2. Journal of Web Semantics1(4), 377–396.

    Google Scholar

    Sirin E., Parsia B., Grau B. C., Kalyanpur A., Katz Y.2007. Pellet: a practical OWL-DL reasoner. Journal of Web Semantics5(2), 51–53.

    Google Scholar

    Semantic Web Rule Language (SWRL). (2004). A Semantic Web Rule Language. Retrieved October, 27, 2010, from http://www.w3.org/Submission/SWRL/.

    Google Scholar

  • Cite this article

    Ourania Hatzi, Dimitris Vrakas, Nick Bassiliades, Dimosthenis Anagnostopoulos, Ioannis Vlahavas. 2013. The PORSCE II framework: using AI planning for automated Semantic Web service composition. The Knowledge Engineering Review 28(2)137−156, doi: 10.1017/S0269888912000392
    Ourania Hatzi, Dimitris Vrakas, Nick Bassiliades, Dimosthenis Anagnostopoulos, Ioannis Vlahavas. 2013. The PORSCE II framework: using AI planning for automated Semantic Web service composition. The Knowledge Engineering Review 28(2)137−156, doi: 10.1017/S0269888912000392
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RESEARCH ARTICLE   Open Access    

The PORSCE II framework: using AI planning for automated Semantic Web service composition

  • 1.

    Department of Informatics and Telematics

  • 2.

    Department of Informatics

  • Published online: 18 February 2013
The Knowledge Engineering Review  28,  2013, 28(2): 137−156  |  Cite this article

Abstract: Abstract: This paper presents PORSCE II, an integrated system that performs automatic Semantic Web service composition exploiting artificial intelligence (AI) techniques, specifically planning. Essential steps in achieving Web service composition include the translation of the Web service composition problem into a solver-ready planning domain and problem, followed by the acquisition of solutions, and the translation of the solutions back to Web service terms. The solutions to the problem, that is, the descriptions of the desired composite service, are obtained by means of external domain-independent planning systems, they are visualized and finally evaluated. Throughout the entire process, the system exploits semantic information extracted from the semantic descriptions of the available Web services and the corresponding ontologies, in order to perform composition under semantic awareness and relaxation.

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    Ourania Hatzi, Dimitris Vrakas, Nick Bassiliades, Dimosthenis Anagnostopoulos, Ioannis Vlahavas. 2013. The PORSCE II framework: using AI planning for automated Semantic Web service composition. The Knowledge Engineering Review 28(2)137−156, doi: 10.1017/S0269888912000392
    Ourania Hatzi, Dimitris Vrakas, Nick Bassiliades, Dimosthenis Anagnostopoulos, Ioannis Vlahavas. 2013. The PORSCE II framework: using AI planning for automated Semantic Web service composition. The Knowledge Engineering Review 28(2)137−156, doi: 10.1017/S0269888912000392
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