Abstract—Several planning techniques in artificial intelligence have been used to perform web service composition (semantic or not), but this process typically uses heuristics based planners combined with search techniques usually too expensive in time solution. In this article, we propose the use of case-based reasoning to reduce the computation times of composition; the model aims to infer from past experience a solution that would guide the selection process during a Web services composition. The proposed methodology also uses a classification defined by an algorithm of semantic similarity technique, in order to compare the new problem, with all previous problems. The previous problem with greatest similarity is accompanied by its corresponding solution and is used to specify which goals already achieved and what remain to achieve for the new problem. The result demonstrates greater efficiency, reducing the search space spending less time.
Index Terms—Composition of semantic web services, planning in artificial intelligence and case-based learning, INDYGO.
Ingrid-Durley Torres is with the Institución Salazar y Herrera from Medellin, Colombia (e-mail: i.torresp@iush.edu.co).
Jaime Guzmán-Luna is with the Director of SINTELWEB Research Group, Universidad Nacional de Colombia, Medellin Campus (e-mail: jaguzman@unal.edu.co).
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Cite:Ingrid-Durley Torres and Jaime Guzmán-Luna, "Applying Case-Based Learning to Improve the Efficiency in the Web Service Compositions," International Journal of Engineering and Technology vol. 6, no. 3, pp. 227-233, 2014.
