Optimizing Causal Link Based Web Service Composition

Automation of Web service composition is one of the most interesting challenges facing the Semantic Web today. Since Web services have been enhanced with formal semantic descriptions, it becomes conceivable to exploit causal links i.e., semantic matching between their functional parameters (i.e., outputs and inputs). The semantic quality of causal links involved in a composition can be then used as a innovative and distinguishing criterion to estimate its overall semantic quality. Therefore non functional criteria such as quality of service (QoS) are no longer considered as the only criteria to rank compositions satisfying the same goal. In this paper we focus on semantic quality of causal link based semantic Web service composition. First of all, we present a general and extensible model to evaluate quality of both elementary and composition of causal links. From this, we introduce a global causal link selection based approach to retrieve the optimal composition. This problem is formulated as an optimization problem which is solved using efficient integer linear programming methods. The preliminary evaluation results showed that our global selection based approach is not only more suitable than the local approach but also outperforms the naive approach.