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.
