With the large number of web services now available via the internet, service discovery, recommendation, and selection have become a challenging and time-consuming task. Organizing services into similar clusters is a very efficient approach. A principal issue for clustering is computing the semantic similarity. Current approaches use methods such as keyword, information retrieval, or ontology-based methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information, and a shortage of high-quality ontologies. Thus, the authors present a method that first adopts ontology learning to generate ontologies via the hidden semantic patterns existing within complex terms. Then, they propose service recommendation and selection approaches based on proposed clustering approach. Experimental results show that the term-similarity approach outperforms comparable existing clustering approaches. Further, empirical study of the prototyping recommendation and selection approaches have proved the effectiveness of proposed approaches.
TopIntroduction
Service Oriented Architecture (Endrei et al., 2004) has been a widely accepted paradigm to facilitate distributed application integration and interoperability. Web services, which share business logic, data and processes through a programmatic interface, are a popular implementation of the service-oriented architecture. Web services are loosely coupled software components and represent an important way for businesses to communicate with each other and with clients. Existing technologies for Web services have been extended to give value-added customized services to users through service composition (Paik et al., 2014). Developers and users can then solve complex problems by combining available basic services such as travel planners. Web service discovery, which aims to match the user request against multiple service advertisements and provides a set of substitutable and compatible services by maintaining the relationship among services, is a crucial part of service composition. Now most of the business organizations are moving towards the Web services. Hence, numbers of Web services publish on the Internet are being increased in recent years (Al-Masri & Mahmoud, 2008). With this proliferation of Web services, service discovery, selection and recommendation are becoming a challenging and time-consuming task because of unnecessary similarity calculations in the matchmaking process within repositories such as Universal Description, Discovery and Integrations (UDDIs) and Web portals. Clustering Web services into similar groups, which can greatly reduce the search space, is an efficient approach to improving performance of service discovery, selection and recommendation. Clustering the Web services enables the user to identify appropriate and interesting services according to his or her requirements while excluding potential candidate services outside the relevant cluster and thereby limiting the search space to that cluster alone. Further, it enables efficient browsing for similar services within the same cluster.
In our previous work, we classified service clustering into several categories by considering the properties used in the clustering process: (i) functionally based clustering (ii) non-functionally based clustering and (iii) social-criteria-based clustering. Most previous works focus on the functionally based clustering approaches, considering the semantics of functional properties such input, output, precondition, and effect (Dasgupta et al., 2011; Nayak & Lee, 2007; Elgazzar et al., 2010). Non-functionally based clustering approaches reduce the computational time and complexity for Web service processes by considering quality-of-service (QoS) properties such as cost and reliability. Social-criteria-based clustering approaches consider social properties of services such as sociability (Chen et al., 2013). In our works, we mainly focused on functionally based clustering. A principal issue for clustering is computing the semantic similarity between services. Recent studies have proposed several approaches to calculating functional similarity. Simple approaches include checking the one-to-one matching of features such as the service name and checking the matching of service signatures such as the messages (Elgazzar et al., 2010). In some studies, information retrieval (IR) techniques are used (Platzer et al., 2009). These include similarity-measuring methods such as search-engine-based (SEB) methods (Liu & Wong 2009) and cosine similarity (Chen et al., 2010; Ma et al., 2008). Some researchers have used logical relationships such as exact and plug-in (Wagner et al., 2011) or edge-counting-based techniques (Xie et al., 2011; Sun, 2010) to increase the semantics in the similarity calculations via ontologies.