Research and Implementation of Service Oriented Architecture for Knowledge Discovery

Knowledge Discovery Service(KDS) is a high level and computation, semantic, knowledge intensive application, requires professional domain knowledge to use, there is much difficulty to realize an end user oriented, intelligent and quality assuring KDS architecture. Current research has proposed data mining ontology and runtime prediction to assist user select correct and high quality service, but data mining ontology only enumerate the method of data mining, fail to ensure the service quality, runtime prediction has not consider the unique characteristic of KDS and the result is always unsatisfying. Aiming at assisting end user self build Knowledge Discovery Application on Service Oriented Architecture(SOA) more effectively, this paper proposes a novel Service Oriented Architecture for Knowledge Discovery SOA4KD. User requirement is divided into content part and quality part. An Extended Knowledge Discovery Task Ontology EKDTO is proposed. Along with Domain Ontology, it can acquire user requirements through natural language interface; A KDS Quality Ontology KDSQO is proposed which consider the unique characteristic of KDS as well as characteristic of general service, meta learning is used to select the most appropriate KDS according to user requirements. Compared with current architecture, This paper proposes some new approaches and techniques for providing end user high quality KDS, and provides a new reference model to implement SOA for Knowledge Discovery.