Hostname: page-component-8448b6f56d-t5pn6 Total loading time: 0 Render date: 2024-04-23T20:22:36.162Z Has data issue: false hasContentIssue false

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

Published online by Cambridge University Press:  18 February 2013

Ourania Hatzi
Affiliation:
Department of Informatics and Telematics, Harokopio University of Athens, Harokopou 89, 17671 Athens, Greece; e-mail: raniah@hua.gr, dimosthe@hua.gr
Dimitris Vrakas
Affiliation:
Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; e-mail: dvrakas@csd.auth.gr, nbassili@csd.auth.gr, vlahavas@csd.auth.gr
Nick Bassiliades
Affiliation:
Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; e-mail: dvrakas@csd.auth.gr, nbassili@csd.auth.gr, vlahavas@csd.auth.gr
Dimosthenis Anagnostopoulos
Affiliation:
Department of Informatics and Telematics, Harokopio University of Athens, Harokopou 89, 17671 Athens, Greece; e-mail: raniah@hua.gr, dimosthe@hua.gr
Ioannis Vlahavas
Affiliation:
Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; e-mail: dvrakas@csd.auth.gr, nbassili@csd.auth.gr, vlahavas@csd.auth.gr

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.

Type
Articles
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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 Services 1(1), 130.CrossRefGoogle 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 International Joint Conference on Artificial Intelligence, San Francisco, CA, USA, 608–620. Morgan Kaufmann Publishers Inc.CrossRefGoogle Scholar
Fox, M., Long, D. 2002. PDDL+: Modelling continuous time-dependent effects. In Proceedings of the 3rd International 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.CrossRefGoogle 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.CrossRefGoogle Scholar
McDermott, D. 2002. Estimated-regression planning for interactions with web services. In Proceedings of the 6th International Conference on Artificial Intelligence Planning Systems, 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.CrossRefGoogle 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 Semantics 1(4), 377396.CrossRefGoogle Scholar
Sirin, E., Parsia, B., Grau, B. C., Kalyanpur, A., Katz, Y. 2007. Pellet: a practical OWL-DL reasoner. Journal of Web Semantics 5(2), 5153.CrossRefGoogle 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