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TopIntroduction
Web services are software components that can enable flexible, low cost and platform-independent application communication and integration (Paolucci, Kawamura, Payne, & Sycara, 2002). The Web service framework is mainly composed of XML-based standards as follows (Curbera et al., 2002):
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SOAP (Simple Object Access Protocol), which is a messaging protocol that facilitates message exchange among services.
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WSDL (Web Service Description Language), which describes the service interface as a set of communication endpoints that enable message exchange.
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UDDI (Universal Description Discovery and Integration), which is a centralized directory of service description.
For Web services to meet the needs of future Web applications, it is essential to enable on-the-fly discovery and composition of services (Agarwal, Handschuh, & Staab, 2003). Unfortunately, the use of existing Web service standards alone does not enable the desired automation and agility of service discovery and composition – primarily because these standards lack the necessary semantic constructs (Sivashanmugam et al., 2003; Sycara, Paolucci, Ankolekar, & Srinivasan, 2003). The utilization of semantics, represented in the form of ontologies, in the area of Web services launched an active research area called “Semantic Web Services” (SWS) (McIlraith, Son, & Zeng, 2001; Sycara et al., 2003).
SWS has attracted increasing attention in computer science and information systems research (Feier, Roman, Polleres, Domingue, & Fensel, 2005; Jacek, Tomas, Carine, & Joel, 2007; Martin et al., 2007). Successful implementation of SWS, however, requires the existence of suitable methods for SWS description (Lara, Roman, Polleres, & Fensel, 2004), catering for service elements such as inputs and outputs annotated using suitable semantic metadata (Verma & Sheth, 2007). In this context, annotation means explicitly referencing the data and functional elements of a service using concepts from shared ontologies. The annotation process is currently performed manually and thus requires comprehensive human involvement. Automating the annotation task is highly desirable as the manual process is tedious, error-prone and difficult (Hepp, 2006; Patil, Oundhakar, Sheth, & Verma, 2004; Rajasekaran, Miller, Verma, & Sheth, 2005).
Few approaches have looked at the problem of semi-automatic annotation. Those approaches that exist can be categorized twofold: First, approaches that automatically build ontologies to represent semantics of given services using learning techniques. Examples of this class of techniques are the approaches of Chifu, Salomie, and Chifu (2007) and Heb and Kushmerick (2003). Second, approaches that require manual development of application ontologies that model implicit semantics of WSDL files. Such application ontologies are then matched against existing domain ontologies using semantic matching techniques in order to find appropriate correspondences that are then used to annotate service data. These approaches are called semantic matching-based approaches. Examples of this category are Patil et al., (2004) and Duo, Juan-Zi, and Bin (2005). Current approaches in both categories have limitations:
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Manual ontology building is difficult and requires extensive technical and domain knowledge. On the other hand, automatic ontology development using learning techniques is still under development and results in ontologies that are of questionable quality.
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Matching-based approaches utilize similarity measurement mechanisms that do not produce precise results when labels of ontological classes and Web service elements are composed of multiple words i.e. Compound Nouns (CNs).