The rapid growth of web services need efficiently discovering the desired web services for the users. Web service interfaces are defined with WSDL that is described by a bag of terms. Many similarity metrics are proposed to solve this problem, it is hardly to resolve the problem that only few pairs of terms between two services have high semantic distance, the semantic distance of other terms between two services are low. Using traditional keyword search metrics may acquire a wrong result that these two web services are similar, in addition, semantics of the web services is hardly to exploit. In this work the authors firstly help the request service to find the services that belong to the same class, and then they use association rule to find terms that are often appear together and find the most similar terms. The authors weaken the weight of the most similar term contained in an association rule and enhance the other terms' weight contained in an association rule to solve the situation above. The experiments show that our approach outperforms some searching methods.
