World wide web service discovery method based on probabilistic latent semantic analysis model

Abstract

The invention discloses a Web service discovery method based on a probabilistic latent semantic analysis model, wherein the probabilistic latent semantic analysis model is utilized to perform modeling analysis on analyzed Web service descriptive documents, semantic concepts hidden behind service description are dug out for semantic clustering, demand service and service-centralized service are subjected to similarity matching at the relatively advanced concept hierarchy, and in combination with spectral clustering on semantic hierarchy, a service data set is subjected to irrelevant data filtration by a spectral clustering-based algorithm prior to the semantic clustering, thereby compressing the computation complexity. As proved by tests, the method is quite superior in both precision ratio and recall ratio of service discovery.

Description

technical field

[0001] The present invention relates to a Web (World Wide Web) service discovery method, which mainly uses the machine learning model of probabilistic latent semantic analysis to mine the semantic concepts hidden behind requests and service descriptions, so as to match and discover services at the concept level. It belongs to the field of information retrieval. Background technique

[0002] Web services emerged as a branch of distributed computing technology and stimulated a new wave of interest from industry and research communities. Web services have the characteristics of self-contained, self-describing and modular applications. Because services adopt open standards and protocols, they are increasingly used to integrate and build business applications on the Internet. With the help of Web services, business organizations can build their own by outsourcing other Web services published on the Internet. commercial applications. With the increasing number o...

Images