Identification of Semistructured Abstract Nonfunctional Properties for Automatic Service Composition

Automatic Service Composition (ASC) provides a new value-added service from existing services by user's request dynamically and automatically. User's requests consist of functional and nonfunctional requirements. During service composition, services that fulfill the functional requirements are located at the discovery stage. Abstract nonfunctional requirements should be identified mainly before the selection stage for service execution. Our research was motivated by the identification of abstract nonfunctional properties (NFPs) for a seamless ASC and proposes transformation from the abstract NFPs to intermediate-level NFPs based on the model of three levels of abstractness of NFPs. To solve the vagueness of the abstractness, we adapt approaches based not only on ontology but also on term similarity. The transformation between the intermediate and the concrete levels is carried out by a deterministic algorithm based on mapping of domain ontology. To evaluate the effectiveness of term similarity metrics for nonterminal terms, vector-based and large corpus-based approaches were investigated. The transformation performance based on precision over our test data set and ontology was evaluated.