Title :
Hybrid cognitive model for semantic discovery and selection of services
Author :
Sharma, Shantanu ; Lather, J.S. ; Dave, Mayank
Author_Institution :
Dept. of Comput. Applic., Nat. Inst. of Technol., Kurukshetra, India
Abstract :
Lack of (semi)automatic mechanisms for service classification in the Universal Description Discovery and Integration repositories and non utilization of explicit or implicit semantic information of a service during its publishing are the two major challenges in the area of web service discovery and selection. We propose a semantic model of human-machine collaboration for the classification, discovery and selection of web services that integrates the semantic as well as syntactic data of the web services to achieve the hybrid cognition. This proposed cognitive approach uses the principals from the machine learning, measures of semantic relatedness and information retrieval where the cognitive information from the WordNet based Omiotis measure of semantic relatedness is merged with the syntactic service profiles and further these semantically enriched service vectors are passed to the supervised learning algorithms to achieve the decision support for the discovery and selection of relevant services. Empirical evaluation of the proposed approach implemented on OWL-X data set has been presented and a comparison of two different supervised classifiers has been made.
Keywords :
Web services; cognitive systems; information retrieval; knowledge representation languages; learning (artificial intelligence); ontologies (artificial intelligence); pattern classification; semantic Web; OWL-X data set; Universal Description Discovery and Integration repositories; Web service discovery; Web service selection; WordNet based Omiotis measure; cognitive approach; cognitive information; decision support; explicit semantic information; human-machine collaboration; hybrid cognition; hybrid cognitive model; implicit semantic information; information retrieval; machine learning; semantic model; semantic relatedness; semantic service discovery; semantically enriched service vectors; service classification; supervised classifier comparison; supervised learning algorithm; syntactic data; syntactic service profile; Conferences; Decision support systems; Machine learning; Measures of Semantic Relatedness; OWL-S; Semantic Web Service Discovery; Text Mining;
Conference_Titel :
Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2014 IEEE International Inter-Disciplinary Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4799-3563-5
DOI :
10.1109/CogSIMA.2014.6816543