Open AccessService identification using k-NN machine learning
Author(s) -
Travis Joseph Poulose,
Sathish Kumar
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.4.13035
Subject(s) - computer science , categorization , service (business) , web service , task (project management) , world wide web , information retrieval , identification (biology) , semantic web stack , semantic web , service discovery , artificial intelligence , botany , biology , economy , management , economics
Web service categorization is a daunting task since it requires semantic descriptions of those services which are not provided to the majority of those websites. The proposal of a Semantic based automated service discovery requires a request from the user that can be analyzed which then provides the user with a list of related webs services based on the request that instigated the search. The problem with these service categorizations listed in the Universal description Discovery and Integration (UDDI) is the way the information is related to one another. The relations follow a syntactic method. Semantic based service descriptions is necessary for accurate web categorization. With the help of machine learning we can also predict the user’s service request automatically based on previous searches and also select the best web service for a particular request that the user has made using a k-nearest neighbor algorithm. By doing this we can distinguish between the various types of user requests, provide services that are suitable for that particular request as well as suggest other services that might potentially suit the needs of the user.