Semantics-based Web service classification using morphological analysis and ensemble learning techniques

International Journal of Data Science and Analytics, Oct 2016

With the emergence of the Programmable Web paradigm, the World Wide Web is evolving into a Web of Services, where data and services can be effectively reused across applications. Given the wide diversity and scale of published Web services, the problem of service discovery is a big challenge for service-based application development. This is further compounded by the limited availability of intelligent categorization and service management frameworks. In this paper, an approach that extends service similarity analysis by using morphological analysis and machine learning techniques for capturing the functional semantics of real-world Web services for facilitating effective categorization is presented. To capture the functional diversity of the services, different feature vector selection techniques are used to represent a service in vector space, with the aim of finding the optimal set of features. Using these feature vector models, services are classified as per their domain, using ensemble machine learning methods. Experiments were performed to validate the classification accuracy with respect to the various service feature vector models designed, and the results emphasize the effectiveness of the proposed approach.

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Semantics-based Web service classification using morphological analysis and ensemble learning techniques

Semantics-based Web service classification using morphological analysis and ensemble learning techniques S. Sowmya Kamath 0 1 V. S. Ananthanarayana 0 1 0 Department of Information Technology, National Institute of Technology Karnataka , Surathkal, Mangalore 575 025 , India 1 V. S. Ananthanarayana With the emergence of the Programmable Web paradigm, the World Wide Web is evolving into a Web of Services, where data and services can be effectively reused across applications. Given the wide diversity and scale of published Web services, the problem of service discovery is a big challenge for service-based application development. This is further compounded by the limited availability of intelligent categorization and service management frameworks. In this paper, an approach that extends service similarity analysis by using morphological analysis and machine learning techniques for capturing the functional semantics of real-world Web services for facilitating effective categorization is presented. To capture the functional diversity of the services, different feature vector selection techniques are used to represent a service in vector space, with the aim of finding the optimal set of features. Using these feature vector models, services are classified as per their domain, using ensemble machine learning methods. Experiments were performed to validate the classification accuracy with respect to the various service feature vector models designed, and the results emphasize the effectiveness of the proposed approach. Web service classification; Supervised machine learning; Natural language processing (NLP); Semantic analysis; Knowledge discovery 1 Introduction Service-oriented computing (SOC) is a distributed computing paradigm that employs fundamental computing entities called services, as constituent elements in developing complex business systems [29]. As per SOC concepts, a business landscape comprised of service-centric applications, called service-oriented architecture (SOA), allows reorganization of business applications and infrastructure as a set of reusable services. In domains such as e-commerce, e-government and B2B,1 Web services are the most popular way of achieving service orientation. Web services use the XML2 standard for encapsulating the data to be exchanged between diverse business platforms. Further, XML-based protocols are also used for data transfer (SOAP3) and for describing the service capabilities (WSDL4). In business ecosystems, most applications are complex, which means that full service orientation can help in designing new applications faster, using existing functionality exposed as services [1]. Hence, the main advantage of a service-oriented application development is that services can be exposed as discoverable software components, thus promoting reusability. For service-based application development, a designer either creates new services or tries to find appropriate existing services for performing the individual tasks as per a defined business workflow. The process of finding existing services, capable of performing a particular task, is called service discovery [14]. Despite considerable research effort in simplifying this process, service discovery is still challenging due to primarily keyword-based search for appropriate 1 Business-to-Business Systems. 2 Extensible Markup Language. 3 Simple Object Access Protocol. 4 Web Service Description Language. services. A unified service registry such as the Universal Business Registry is no longer available, and Web services are currently available in some service portals such as ProgrammableWeb and BioCatalogue or directly from service providers’ websites [21]. These service portals mostly provide keyword searching and manual categorization, due to which finding the most relevant services for a given task is still challenging. There may be several services already developed by third-party developers which may be very well suited for the given task that did not even appear in the search results due to these issues. The problem of adding semantics and machine understanding to Web service capabilities to support automated dynamic discovery, matchmaking, composition and recommendation [9] has remained an area of active research interest. The primary motivation for semanticizing data and services on the Web is to facilitate seamless interoperation and knowledge discovery over the Web [2,12]. However, at present, semantically enhanced published services are very few and the task of adding semantics to those lacking may prove to be quite a monumental job, in terms of time and cost. Therefore, alternate methods that are not dependent on the immediate availability of semantic markup, but can still overcome the problems associated with keyword-based service discovery, are the need of the day. In this paper, we use different feature vectors selection techniques to represent a service document in vector space, with the aim of finding (...truncated)


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S. Sowmya Kamath, V. S. Ananthanarayana. Semantics-based Web service classification using morphological analysis and ensemble learning techniques, International Journal of Data Science and Analytics, 2016, pp. 61-74, Volume 2, Issue 1-2, DOI: 10.1007/s41060-016-0026-x