MPSWS: a multi-agent personalisation of the semantic web services

Abstract

On the web, finding a service which is suited to user requirements is essential. In this paper, a multi-agent personalisation of the SWS system, MPSWS, is introduced. In MPSWS, both user information and SWS are interacting to identify user needs. It exploits agent-based systems to support the interaction between users and semantic web services in dynamic configurations. The work achieves the following contributions: 1) proposing a novel system architecture that realises personalisation in the semantic web services; 2) applying an AQ machine learning algorithm to proactively identifying user intentions from past events; 3) providing a BDI-based problem solver that exploits the user's context to determine his intentions; 4) building up OWLS queries for each identified user intention, which are processed by SWS matchmaker, to identifying OWLS services that can semantically match user needs. MPSWS is experimentally tested and its performance at different operational conditions is evaluated.