Ranking semantic web services by matching triples and query based on similarity measure
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ORIGINAL RESEARCH
Ranking semantic web services by matching triples and query based on similarity measure M. Santhoshkumar1
•
S. Sagayaraj1
Received: 4 February 2019 / Accepted: 20 June 2019 Bharati Vidyapeeth’s Institute of Computer Applications and Management 2019
Abstract A Web Service (WS) provides interoperability among platforms. To find out a desired web service that matches the user requirements are difficult, which raises the need for the WS discovery tool. This paper proposes a tool for Semantic Web Service Discovery (SWSD) to calculate Semantic Web Service (SWS) similarity values between the Requested Query (RQ) and Triples (Tr) from OWL-S file. SWSD uses processing components such as Parts of Speech (POS), WordNet Dictionary, and WordNet Sense (WNS) for reducing the errors in the matching. This matching determines the similarity Threshold (T) using Matching Rules (MR), and then calculates the average T values, which are ordered and ranked. This paper also analyses results in two phases such as ranking the services and QRT. When the Tr values increases, the QRT values are also increases. The SWSD results are better than existing framework results based on the above MR, Test Case, and QRT. These will help the user to find out the desired SWS. Keywords Triples extraction Test case Matching rules for SWSD tool SWSD components
& M. Santhoshkumar [email protected] S. Sagayaraj [email protected] 1
Sacred Heart College, Tirupattur, Vellore, India
1 Introduction There are two types of services available on the internet such as Web Services and Semantic Web services. WS provides a standard means for the exchange of operations between procedures in networks. After completing the operations numerous platform applications are organized as the services through its exclusive URL (Uniform Resource Locator) [1]. The WS is under the Non-Semantic Web Services (NSWS) and the description of the same is readable by the human. It is an infrastructure developed by the group of industries and individuals for service discovery. The NSWS discovery involves the method by keyword search in Service Search Engine (SSE). The service provider publishes the NSWS through their websites and also used by registries or agents. These facilities are utilized by SSE in WS discovery [2]. For instance, the login services such as admin login, user login, guest login, etc., if ‘‘login’’ keyword is searched that will match with prefix or suffix words by SSE and provides number of unrelated login services. It is not clear for the user to decide on selecting a suitable service equivalent to their requirements [3]. Hence, user is ignorant to find the difference between related and unrelated services in the Internet. SWS is the extension of the WS. It provides a new level of exchange of information, using adding encoded semantic explanations to specific business functionalities, thereby enabling automatic service discovery, sensing and ranking. The current trends in the research community are to exploit Semantic Web technologies, such
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