1. Deciding Semantic Matching of Stateless Services Duncan Hull †, Evgeny Zolin †, Andrey Bovykin ‡, Ian Horrocks †, Ulrike Sattler † and Robert Stevens † ABSTRACT A novel technique for semantically matching service requests with advertisements is described. This technique extends current approaches by explicitly stating the relationship between the input and output of a given service. The meaning of the terms used in the description is defined using OWL (the Web Ontology Language) and this has several advantages outlined below. Acknowledgements: This work was supported by the my Grid UK e-Science programme EPSRC GR/R67743/01 and GR/S63168/01 http://dynamo.man.ac.ukhttp://dynamo.man.ac.uk Dynamo Project http://taverna.sourceforge.nethttp://taverna.sourceforge.net Taverna project http://www.mygrid.org.ukhttp://www.mygrid.org.uk myGrid project. † School of Computer Science, University of Manchester, UK. first.last@cs.man.ac.uk ‡ Department of Computer Science, University of Liverpool, UK. andrey@csc.liv.ac.uk @ American Association of Artificial Intelligence (AAAI) 2006, Boston, MA, USA REFERENCES 1.Duncan Hull, Katy Wolstencroft, Robert Stevens, Carole Goble, Matthew Pocock, Peter Li and Tom Oinn (2006 ) Taverna: A tool for building and running workflows of services. Nucleic Acids Research 34: W729-W732 (Web Server Issue) 2.Duncan Hull, Evgeny Zolin, Andrey Bovykin, Ian Horrocks, Ulrike Sattler and Robert Stevens (2006) Deciding Semantic Matching of Stateless Services. Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06), Boston, MA, USA, July 16-20 3.Dean Martin et al (2004) Bringing Semantics to Web Services: The OWL-S approach in Proceedings of First International Workshop on Semantic Web Services and Web Process Composition (SWSWPC 2004) INTRODUCTION Understanding and managing the data generated from Human Genome Project is recognised as a grand challenge for both computer science and biomedicine. All of the raw data, and many of the tools to interpret and analyse it, are publicly available as Web Services. As of 2006, around 3000 highly heterogeneous and autonomous services are available from within client applications like the Taverna workbench [1], part of the my Grid project. Current techniques for matching service requests with advertisements for biomedical services have proved inadequate. This poster, and accompanying paper [2], outlines a novel technique for semantically matching service requests with service advertisements. We demonstrate that this matching technique improves both precision and recall of web service matching. The description of the relationship between input and output, allows two similar advertisements that have the same inputs and outputs, but perform different functions to be distinguished from each other - this enables more precise matching, minimising false negatives. RELATED WORK Several related research projects use logic- based knowledge representation to advertise and find Web Services in registries: The OWL-S profile desribes IOPEs (Iinputs, outputs, preconditions and effects) but currently has no way of relating inputs to outputs due to limitations in the OWL language. Since many of our services are stateless, we do not need stateful descriptions that OWL-S provides. BioMOBY and my Grid advertise services with a related language (RDF and RDFS) that is less expressive than OWL and can’t relate inputs to outputs at the class level The Web Services Modelling Ontology (WSMO) has a mechanism for relating intputs to outputs, but as far as we know, no decidability results exist for this approach EXAMPLES The illustrations below show a sample advertisement with two related service requests, using a commonly used standard example of Wine buying from [3] OUTPUT: Wine INPUT: GeoRegion LocatedIn Produces WineGrower SERVICE: Service advert INPUT: g GeoRegion OUTPUT: w Wine THERE IS SOME f [WineGrower f, LocatedIn(f,g), Produces(f,w)] Service request 2 INPUT: g FrenchGeoRegion OUTPUT: w FrenchWine THERE IS SOME f [WineGrower f, LocatedIn(f,g), Produces(f,w)] Service request 1 INPUT: g GeoRegion OUTPUT: w Wine THERE IS SOME s [Shop s, LocatedIn(s,g), Sells(s,w)] The ability to use a background ontology during service matching, allows us to increase the recall of matching algorithms by reasoning that Wines producedIn FrenchGeoRegion are FrenchWines CONCLUSIONS We are currently implementing the approach described here in a public registry of biomedical services. Future work will investigate using this technique to describe and retrieve compositions of services e.g. Workflows, which are commonly used by scientists conducting experiments on genomic and proteomic data on the web. No match Match (graphically) The approach described here complements and extends each of the above. In addition the service matching problem is reducible to subsumption of conjunctive queries, hence it is decidable for many Description Logics [2].