Non-Monotonic Modeling for Personalized Services Retrieval and Selection

Abstract

With growing interest in Semantic Web services and emerging standards, such as OWL, WSMO, and SWSL in particular, the importance of applying logic-based models to develop core elements of the intelligent Semantic Web has been more closely examined. However, little research has been conducted in Semantic Web services on issues of non-mono-tonicity and uncertainty of Web services retrieval and selection. In this paper, the authors propose a non-monotonic modeling and uncertainty reasoning framework to address problems related to adaptive and personalized services retrieval and selection in the context of micro-payment processing of electronic commerce. As intelligent payment service agents are faced with uncertain and incomplete service information available on the Internet, non-monotonic modeling and reasoning provides a robust and powerful framework to enable agents to make service-related decisions quickly and effectively with reference to an electronic payment processing cycle.