A Hybrid Similarity Measure for Dynamic Service Discovery and Composition based on Mobile Agents

With the ever-present competition among companies, the prevalence of web services (WSs) is increasing dramatically. This leads to the diversity of the similar services and their developed nature, which makes the discovery of a relevant service during the composition phase a complex task. Since most of the competition companies aim to discover high-quality services with minimum charges in order to increase the number of customers and their profit. The semantic WSs allow performing dynamic service discovery through the entities software and intelligent agents. However, the solutions provided to the discovery process are limited to their performance in terms of the quickness to respond to the request in real-time, without considering the constraints such as the accuracy in the discovery phase and the quality of the similarity mechanism evaluation. They usually are based on the similarity measure of distance between concepts in the ontology instead of taking into consideration the relationships semantically and the strength of the semantic relationship between concepts in the context. In this paper, we proposed a novel hybrid semantic similarity method to improve the service discovery process. The hybrid method is applied to an architecture based on mobile agents, where cooperative agents are integrated to facilitate and speed up the discovery process. In the first hybrid method, we defined the Latent Semantic Analysis (LSA) with a semantic relatedness measure to avoid the ambiguity of the terms and obtain a purely semantic relatedness at level of the service description. The second one is defined to analyze the relationships at the level of the I/O service based on the subsumption reasoning, called IO-MATCHING. Experimental results on a real data set demonstrate that our solution outperforms the state-of-the-art approaches in terms of precision, recall, F-measure, and consumed time of the service discovery.