Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-04T21:18:58.495Z Has data issue: false hasContentIssue false

Problem solving methods in a global networked age

Published online by Cambridge University Press:  14 October 2009

John Domingue
Affiliation:
Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom
Dieter Fensel
Affiliation:
Semantic Technology Institute Innsbruck, University of Innsbruck, Innsbruck, Austria

Abstract

We believe that the future for problem solving method (PSM) derived work is very promising. In short, PSMs provide a solid foundation for creating a semantic layer supporting planetary-scale networks. Moreover, within a world-scale network where billions services are used and created by billions of parties in ad hoc dynamic fashion we believe that PSM-based mechanisms provide the only viable approach to dealing the sheer scale systematically. Our current experiments in this area are based upon a generic ontology for describing Web services derived from earlier work on PSMs. We outline how platforms based on our ontology can support large-scale networked interactivity in three main areas. Within a large European project we are able to map business level process descriptions to semantic Web service descriptions, to enable business experts to manage and use enterprise processes running in corporate information technology systems. Although highly successful, Web service-based applications predominately run behind corporate firewalls and are far less pervasive on the general Web. Within a second large European project we are extending our semantic service work using the principles underlying the Web and Web 2.0 to transform the Web from a Web of data to one where services are managed and used at large scale. Significant initiatives are now underway in North America, Asia, and Europe to design a new Internet using a “clean-slate” approach to fulfill the demands created by new modes of use and the additional 3 billion users linked to mobile phones. Our investigations within the European-based Future Internet program indicate that a significant opportunity exists for our PSM-derived work to address the key challenges currently identified: scalability, trust, interoperability, pervasive usability, and mobility. We outline one PSM-derived approach as an exemplar.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2009

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

REFERENCES

Benjamins, V.R., Plaza, E., Motta, E., Fensel, D., Studer, R., Wielinga, B., Schreiber, G., Zdrahal, Z., & Decker, S. (1998). IBROW3: an intelligent brokering service for knowledge-component reuse on the world-wide web. Proc. 11th Banff Knowledge Acquisition for Knowledge Based System Workshop (KAW98), Banff, Canada, 1998.Google Scholar
Chandrasekaran, B. (1986). Generic tasks in knowledge based reasoning: high-level building blocks for expert systems design. IEEE Expert 1(3), 2330.CrossRefGoogle Scholar
Cimpian, E., Vitvar, T., Zaremba, M., Moran, M., & Oren, E. (2005, February). Overview and scope of WSMX. WSMX Working Draft D13.0v0.2.Google Scholar
Clancey, W.J. (1983). The epistemology of a rule based expert system—a framework for explanation. Artificial Intelligence 20, 215251.CrossRefGoogle Scholar
Clancey, W.J. (1985). Heuristic classification. Artificial Intelligence 27, 289350.CrossRefGoogle Scholar
Clark, D.D., Partridge, C., Ramming, C.J., & Wroclawski, J.T. (2003). A knowledge plane for the Internet. Proc. ACM SIGCOMM.CrossRefGoogle Scholar
Crubézy, M., Musen, M.A., Motta, E., & Lu, W. (2003). Configuring online problem-solving resources with the Internet reasoning service. IEEE Intelligent Systems 18(2), 3442.CrossRefGoogle Scholar
Darr, T., Klein, M., & McGuiness, D.L. (1998). Special Issue: configuration design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 12, 293294.CrossRefGoogle Scholar
de Kleer, J., & Brown, J.S. (1984). A qualitative physics based on confluences. Artificial Intelligence 24, 783.CrossRefGoogle Scholar
Domingue, J., Cabral, L., Galizia, S., Tanasescu, V., Gugliotta, A., Norton, B., & Pedrinaci, C. (2008). IRS-III: a broker based approach to Semantic Web services. Journal of Web Semantics 6(2), 109132.CrossRefGoogle Scholar
Fensel, D., Bussler, C., Ding, Y., & Omelayenko, B. (2002). The Web services modeling framework WSMF. Electronic Commerce Research and Applications 1(2).CrossRefGoogle Scholar
Fensel, D., Lausen, H., Polleres, A., de Bruijn, J., Stollberg, M., Roman, D., & Domingue, J. (2006). Enabling Semantic Web Services: The Web Service Modelling Ontology. Berlin: Springer.Google Scholar
Fensel, D., & Motta, E. (2001). Structured development of problem solving methods. IEEE Transactions on Knowledge and Data Engineering 13(6), 913932.CrossRefGoogle Scholar
Galizia, S. (2006). WSTO: a classification based ontology for managing trust in Semantic Web Services. Proc. 3rd European Semantic Web Conf., Budva, Montenegro.CrossRefGoogle Scholar
Galizia, S., Gugliotta, A., & Domingue, J. (2007). A trust based methodology for web service selection. Proc. 1st IEEE Int. Conf. Semantic Computing, Irvine, CA.CrossRefGoogle Scholar
Gugliotta, A., Domingue, J., Cabral, L., Tanasescu, V., Galizia, S., Davies, R., Gutierrez Villarias, L., Rowlatt, M., Richardson, M., & Stincic, S. (2008). Deploying Semantic Web services based applications in the e-government domain. Journal on Data Semantics 10, 96132.Google Scholar
Hepp, M., Leymann, F., Domingue, J., Wahler, A., & Fensel, D. (2005). Semantic business process management: a vision towards using Semantic Web services for business process management. Proc. IEEE Int. Conf. e-Business Engineering (ICEBE 2005), pp. 535540, Beijing.CrossRefGoogle Scholar
Keller, G., Nuttgens, M., & Scheer, A.W. (1992). Semantische Processmodellierung auf der Grundlage Ereignisgesteuerter Processketten (EPK). University of Saarland, Saarbrucken, Veroffentlichungen des Instituts fur Wirtschaftsinformatik, Heft 89.Google Scholar
McDermott, J. (1980). R1: an expert in the computer systems domain. Proc. AAAI 1980, pp. 269271.Google Scholar
Marcus, S., Stout, J., & McDermott, J. (1988). VT: an expert elevator designer that uses knowledge based backtracking. AI Magazine 9(1), 95112.Google Scholar
McDermott, J. (1988). Preliminary steps toward a taxonomy of problem-solving methods. In Automating Knowledge Acquisition for Experts Systems (Marcus, S., Ed.). Boston: Kluwer Academic.Google Scholar
Medeiros, A., Pedrinaci, C., Aalst, W., Domingue, J., Song, M., Rozinat, A., Norton, B., & Cabral, L. (2007). An outlook on semantic business process mining and monitoring. Proc. 3rd Int. IFIP Workshop on Semantic Web and Web Semantics (SWWS ’07), Move Federated Conferences and Workshops.CrossRefGoogle Scholar
Motta, E. (1999). Reusable Components for Knowledge Modelling. Amsterdam: IOS Press.Google Scholar
Motta, E., Domingue, J., Cabral, L., & Gaspari, M. (2003). IRS-II: a framework and infrastructure for Semantic Web services. Proc. 2nd Int. Semantic Web Conf. (ISWC 2003). Lecture Notes in Computer Science, Vol. 2870. Berlin: Springer.CrossRefGoogle Scholar
Motta, E., O'Hara, K., Shadbolt, N., Stutt, A., & Zdrahal, Z. (1996). Solving VT in VITAL: a study in model construction and knowledge reuse. International Journal of Human–Computer Studies 44, 333371.CrossRefGoogle Scholar
Motta, E., & Zdrahal, Z. (1996). Parametric design problem solving. Proc. 10th Knowledge Acquisition for Knowledge Based Systems Workshop, Banff, Canada.Google Scholar
Musen, M.A., Fergerson, R.W., Grosso, W.E., Noy, N.F., Crubézy, M., & Gennari, J.H. (2000). Component based support for building knowledge–acquisition systems. Proc. Conf. Intelligent Information Processing (IIP 2000) of the Int. Federation for Information Processing World Computer Congress (WCC 2000), pp. 1822. Accessed at www-smi.stanford.edu/pubs/SMI_Reports/SMI-2000-0838.pdfGoogle Scholar
Newell, A. (1982). The knowledge level. Artificial Intelligence 18, 87127.CrossRefGoogle Scholar
Nitzsche, J., Wutke, D., & van Lessen, T. (2007). An ontology for executable business processes. Proc. Workshop on Semantic Business Process and Product Lifecycle Management (SBPM-2007), CEUR-WS.Google Scholar
Norton, B., Pedrinaci, C., Domingue, J., & Zaremba, M. (2008). Semantic execution environments for semantics-enabled SOA. In IT-Methods and Applications of Informatics and Information Technology, Vol. 2, pp. 118121. Oldenbourg, Germany: Wissenschaftsverlag.Google Scholar
OMG. (2002). The object management group: meta-object facility, version 1.4. Accessed at http://www.omg.org/technology/documents/formal/mof.htmGoogle Scholar
OMG. (2006). Business process modeling notation specification 1.0. Accessed at http://www.bpmn.org/Documents/OMG%20Final%20Adopted%20BPMN%201-0%20Spec%2006-02-01.pdfGoogle Scholar
Pedrinaci, C., Domingue, J., Brelage, C., van Lessen, T., Karastoyanova, D., & Leymann, F. (2008). Semantic business process management: scaling up the management of business processes. Proc. Int. Conf. Semantic Computing (ICSC 2008), Santa Clara, CA.CrossRefGoogle Scholar
Pedrinaci, C., Domingue, J., & Medeiros, A. (2008). A core ontology for business process analysis. Proc. 5th European Semantic Web Conf., Tenerife, Spain.CrossRefGoogle Scholar
Roman, D., Lausen, H., Keller, U., de Bruijn, J., Bussler, C., Domingue, J., Fensel, D., Hepp, M., Kifer, M., König-Ries, B., Kopecky, J., Lara, R., Oren, E., Polleres, A., Scicluna, J., & Stollberg, M. (2006). Web Services Modeling Ontology (WSMO). WSMO Working Draft D2v1.3. Accessed at http://www.wsmo.org/TR/d2/v1.3/Google Scholar
Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt, N., Van de Velde, W., & Wielinga, B. (1999). Knowledge Engineering and Management: The CommonKADS Methodology. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Shadbolt, N., Motta, E., & Rouge, A. (1993). Constructing knowledge based systems. IEEE Software 10(6), 3438.CrossRefGoogle Scholar
Shortliffe, E.H. (1974). MYCIN: a rule based computer program for advising physicians regarding antimicrobial therapy selection. PhD dissertation, Stanford University.Google Scholar
SOAP. (2003). SOAP Version 1.2 Part 0: Primer. Accessed at http://www.w3.org/TR/soap12-part0/Google Scholar
Tanasescu, V., Gugliotta, A., Domingue, J., Gutiérrez Villarías, L., Davies, R., Rowlatt, M., Richardson, M., & Stincic, S. (2007). Geospatial data integration with Semantic Web services: the emerges approach. In The Geospatial Web (Scharl, A., & Tochtermann, K., Eds.). New York: Springer.Google Scholar
UDDI. (2003). UDDI spec technical committee specification v. 3.0. Accessed at http://uddi.org/pubs/uddi-v3.0.1-20031014.htmGoogle Scholar
Wiederhold, G. (1992). Mediators in the architecture of future information systems. IEEE Computer 25(3), 3849.CrossRefGoogle Scholar
Wielinga, B.J., Schreiber, A.Th., & Breuker, J.A. (1992). KADS: a modelling approach to knowledge engineering. Knowledge Acquisition 4(1), 553.CrossRefGoogle Scholar
WSDL. (2001). Web Services Description Language (WSDL) 1.1. Accessed at http://www.w3.org/TR/2001/NOTE-wsdl-20010315Google Scholar