Web Service Discovery Based on Knowledge Graph and Similarity Network

Service discovery aims to address the problem of service information explosion and find and locate services that meet the needs of service requesters. Because service description information is mostly composed of short text with noise and has the characteristics of semantic sparseness, it is difficult to extract the implied context information of service description. This paper proposes a service discovery framework based on Knowledge graphs and neural Similarity Network (KSN). Which uses knowledge graphs to connect entities to obtain rich external information to enhance the semantic information of service descriptions. convolutional neural network and similarity network is utilized to extract context information. Through extensive experiments on a real service data set show that KSN is superior to existing web service discovery methods in terms of multiple evaluation metrics.