Aggregating Web Service matchmaking variants using web search engine and machine learning

Variety of Web Service discovery algorithms had been investigated for improvement of the retrieval quality. Combining the several algorithms according to their strong points, is proposed as enabling more refined discovery consequence. Now, many researches as OWL-Mx are sited as examples, had already shown the method that join together and conclude for the specific domain. However, there are no way to conclude multi-algorithms results. Klusch shows the brand-new way that leads the conclusion by using machine-learning algorithm Support Vector Machine (SVM). In this research, we attempted to apply the SVM aggregation and several new discovery algorithm using similarity based on search engine, shown on Trip Domain service discovery. And, 88 percent over score of precision, were gotten as the result from specifically prepared queries for Trip Domain. This experiment also had shown 10 percent missing which occurred by using web page count based similarity computation. In future work, we will conduct some comparison for getting more reliability of this proposed method.