A Method of Web Service Discovery Based on Semantic Message Bipartite Matching for Remote Medical System

By Zhang, Yang; Liu, Bing-Yue et al. | Journal of Theoretical and Applied Electronic Commerce Research, August 2009 | Go to article overview

A Method of Web Service Discovery Based on Semantic Message Bipartite Matching for Remote Medical System


Zhang, Yang, Liu, Bing-Yue, Wang, Hong, Journal of Theoretical and Applied Electronic Commerce Research


Abstract

The number of Web services are growing at an explosive speed, which brings great challenges to the accurate, efficient and automatic retrieval of target services for users. This paper presents a service discovery method with semantic matchmaking which could be used in remote medical systems. Adding ontology related semantic annotations to service interfaces is considered, and a method of service discovery based on bipartite matching of semantic message similarity is proposed. The method is easy to implement because it is not limited to specific service model. It also contributes to the improvement of service discovery efficiency when service is retrieved in an automatic way.

Key words: Web Service Discovery, Remote Medical Systems, Message Bipartite Matching, Semantic, Similarity

(ProQuest: ... denotes formulae omitted.)

1 Introduction

Network technologies offer new opportunities for wide adaptation of new medical technologies and development of telemedicine or remote medical systems. By making use of these technologies, we can quickly gather information and process it in various ways in order to assist with making diagnosis and treatment decisions immediately and accurately no matter where the patient may geographically be in the world.

According to the features of remote medical solution over the Internet, Service-Oriented Architectures (SOA) and Web service technologies have been proposed to respond to some interoperability challenges of heterogeneous medical systems. Web service technology incorporates the strengths of distributed computing, grid computing, Extensible Makeup Language (XML) and so on. By adopting XML-based specifications such as Web Service Description Language (WSDL), Universal Description, Discovery and Integration (UDDI) and Simple Object Access Protocol (SOAP), Web service technology is well suited to the integration and implementation of heterogeneous remote medical systems by providing interoperability among diverse applications and platform and language independent interfaces.

The number of Web services is growing at an explosive speed, which brings great challenges to the accurate, efficient and automatic retrieval of target services for users. Some semantic matchmaking approaches have been proposed to support automatic service discovery [1], [5] - [7], [14]. In [1], semantic temporal and security constraints for service discovery are considered. The work in [7] extends existing approaches by supporting semantic approximate matching and IR techniques. The work in [14] proposes (Quality of Service) QoS-based selection of services. In [6], the use of graph transformation rules for specifying both queries and services is proposed. The work in [5] suggests behavior matching for service discovery based on similarity measures of graphs.

2 Example Web Services in Remote Medical System

We motivate our automatic service discovery method through the following example.

A web service, w, has typically two sets parameters: w^sup i^ = {i1, i2,?} for SOAP request (as input) and w^sup o^ ={o1,o2,?} for SOAP response (as output). When w is invoked with all input parameters, w^sup i^ , it returns the output parameters w^sup o^. We assume that in order to invoke w, all input parameters in wi must be provided (i.e, w^sup i^ are mandatory).

Consider the two Web services in table1 illustrated in WSDL notation:

1. Given the disease type, current location and expert or not, findDoctor returns the hospital, phone and email.

2. Given the sickness type and current region, getPhysician returns the email and phone.

Now, consider the following requests:

1. r1: sequential input parameters are "pleurisy, Beijing, true", and sequential output parameters are the hospital, phone and email of a doctor.

2. r2: sequential input parameters are "arthritis, Shanghai", and sequential parameters are the email and phone of a doctor. …

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