Abstract
Abstract Automatic services collaboration calls for the development of semantically structured service network to maximize the utility of Web services. Service Semantic Link Network (S-SLN) is the semantic model for effectively managing Web service resources by the dependency relationship between services. We provided an effective method for constructing S-SLN based on the graphical structure representation of the dependencies embedded in a probabilistic model. A Markov network is an undirected graph whose links represents probability dependencies. We first learned Markov network structure from Web services data, and then transformed the undirected Markov network structure into a directed graph structure of S-SLN based on the same joint probability distribution. Finally, experimental results show the effectiveness of the method.
Highlights
The rapid increase in the amount of Web service produced in recent years on the Web has resulted in a more sophisticated service process for e-Commerce, which involving numerous interacting business objects within complex distributed processes
In this paper we proposed and explored a graphical model based methodology for constructing a service semantic link network of semantically and functionally related Web services
We have presented a simple definition of semantically structured network Service Semantic Link Network (S-Semantic Link Network (SLN)) based on SLN and discussed an approach of S-SLN construction that are used to analyze, and explore such a network for potentially improving the service discovery process and service collaboration
Summary
The rapid increase in the amount of Web service produced in recent years on the Web has resulted in a more sophisticated service process for e-Commerce, which involving numerous interacting business objects within complex distributed processes. In order to effectively collaborate between services, Web services need to be organized and their functionalities semantically described This fact calls for the development of automatic methods for service relationship discovery and their dependency structure in order to maximize the utility of Web services by making them widely available to the community. S-SLN is the underlying semantic model for effectively implementing automatic Web service search and composition by a relationship dependency network which connects services with different types of relationships. When manually assigned annotation relationship tags of related services are not available, we hypothesize that automated approaches could be used to improve the SSLN discovery process These include building networks of related services. We propose a methodology to discover service semantic link networks, which can help to identify related service resources on the basis of their inherent service relationship dependencies.
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