Research On Service Clustering Integrating Functional Semantics And Process Collaboration Similarity

With the application and popularization of service-oriented architecture(SOA)in the process of software development,a large number of Web services have emerged on the Internet.It is still an urgent question of how to organize and manage these services and find services that meet the needs of users from similar services in the field of service computing.In order to improve the accuracy and efficiency of service search,service clustering is usually used to reduce the search space of services.The current service clustering methods mainly focus on the calculation of service function similarity.When calculating the function similarity,the topic model is usually used to extract the functional features of the service description text.However,due to the sparse words in the service description text,the quality of the generated service functional vectors is not high,which affects the clustering effect.In addition,the current research on service clustering lacks consideration of the service cooperation relationship,and fails to well integrate the cooperation similarity between services into the clustering process.To address the above problems,this paper proposes a service clustering method that integrates functional semantics and process collaboration relationship measurement.The main work and innovations are as follows:(1)The existing traditional extraction methods present low effectiveness in extracting feature words when faced with high sparse data.Aiming at the problem,this thesis innovates a word extraction method that introduces attributes include part of speech and semantic weight to jointly represent the importance of words,so as to obtain high-quality feature words.Based on this,the GSDMM topic model is used to generate functional semantic vectors for the extracted feature words,so as to generate high-quality service function vectors and improve the calculation accuracy of the semantic similarity of service functions.(2)The service process collaboration graph is established according to the process collaboration relationship between services,and the process collaboration vector is generated for the services in the process collaboration graph based on Node2 Vec.The cooperation similarity between services is calculated by the cooperation vector,and the service cooperation similarity is introduced into the service clustering,further improve the quality of the service clustering.(3)A FPK-means++ clustering algorithm that integrates functional semantics and process collaboration similarity is constructed.By adjusting the ratio of functional semantic similarity and process collaboration similarity in the process of similarity calculation to obtain a superior clustering effect.Experiments verify the effectiveness and advancement of the method in this paper.