SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
A message-driven-based semantic web service composition model
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
QoS-based service optimization using differential evolution
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Privacy-aware DaaS services composition
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
Engineering Applications of Artificial Intelligence
Reconfigurable composition of web services using belief revision through genetic algorithm
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
An Optimal and Complete Algorithm for Automatic Web Service Composition
International Journal of Web Services Research
Web Intelligence and Agent Systems
Decentralized multi-agent service composition
Multiagent and Grid Systems
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Semantic web service composition is about finding services from a repository that are able to accomplish a specified task if executed. The task is defined in a form of a composition request which contains a set of available input parameters and a set of wanted output parameters. Instead of the parameter values, concepts from an ontology describing their semantics are passed to the composition engine. The parameters of the services in the repository the composer works on are semantically annotated in the same way as the parameters in the request. The composer then finds a sequence of services, called a composition. If the input parameters given in the request are provided, the services of this sequence can subsequently be executed and will finally produce the wanted output parameters. In this paper, three different approaches to semantic web service composition are formally defined and compared with each other: an uninformed search in form of an IDDFS algorithm, a greedy informed search based on heuristic functions, and a multi-objective genetic algorithm.