KSN:A Web Service Discovery Method Based on Knowledge Graph and Similarity Network

Service discovery aims to solve the problem of service information explosion,find and locate services that meet the needs of service requesters.Since the service description information is mainly composed of short text with noise and has the feature of sparse semantics,it is difficult to extract the implicit context information of the service description document.In addition,the traditional service discovery method directly obtains the characteristic representation of the service.According to the cosine similarity to calculate the similarity,the used measurement function is not in line with human perception.In response to the above two problems,this paper proposes a service discovery framework (KSN) based on knowledge graphs and neural similar networks.It uses the knowledge graph to connect the entities in the service description and specifications to obtain rich external information,thereby enhancing the semantic information of the service description.And it uses convolutional neural network (CNN) to extract the feature vector of the service as the input of the neural similarity network.The neural similarity network will learn a similarity function to calculate the similarity between the service and the request to support the service discovery process.A large number of experiments on real service data sets crawled by ProgrammableWeb show that KSN is superior toexisting Web service discovery methods in terms of multiple evaluation metrics.