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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15458

Title: Agent-based approach semantic web services personalization
Authors: G. Shorbagy
M. Zaki
A. Rafea
M. Maree
Keywords: Semantic web services; Agent technology, Personalization, Ontology, Context awareness, Machine learning.
Issue Date: 2011
Publisher: International journal of intelligent computing and information sciences
Abstract: In the last few years, Semantic Web Community proposes standards and semantic representations of the Web Services, DAML-S and OWL-S are two standards examples of semantic representations of web services that are used in the last few years. These standards are ontologies that support web services depending on DAML markup language standard. Traditional personalization of these web services is a problem. Because personal (user) information is hardly utilized to make the interaction with the user context more efficient for satisfying user needs. Nowadays many efforts are done to personalize the web service and the semantic web services (SWS). These efforts allow adding user context as a part of the web service description. The cornerstone of SWS is the Matchmaker/Broker agent, which hardly utilizes the user context and the personal needs in the web service. In this work, the Multi-agent Personalization of the SWS (MPSWS) approach is introduced. In MPSWS, both user information and SWS are interactively to identify user needs. The aim of this research is to present an efficient interaction way between two technologies, namely personal producing (user agents) systems and personal consumer (SWS) systems. This approach supports and realizes the personalization in the interaction between the user itself and the SWS. This research explores how agent-based systems can support the interaction between users and semantic web services in a dynamic configuration and social settings characteristics. More specifically, our work aims to advances the following contributions: (1)proposing a system architecture that supports personalization in the semantic web services; (2)applying an efficient problem solvers based on the user’s context to determine his/her intentions; (3)applying an efficient machine learning mechanism to ‘automatically’ identify user intensions from the history events; (4) building personal OWLS queries, which are processed by SWS matchmaker, to identifying semantic web services that support user needs; (5) applying an efficient mechanism to manipulate the matchmaker results (i.e. user needs) and to provide them to the user.
URI: http://hdl.handle.net/123456789/15458
Appears in Collections:College of Computer and Information Sciences

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