Description

As one foundational technology of cloud computing, services computing is playing a critical role to enable provisioning of software as a service (SaaS). However, how to effectively and efficiently discover proper available services from the cloud of resources remains a big challenge. This project reports our continuous efforts on semantic services discovery. We extend the Support Vector Machine (SVM)-based text clustering technique in the context of service oriented categorization in a service repository, and propose an iterative process to incrementally enrich domain ontology. A popular Web 2.0 mash up platform is used as a testbed; and preliminary evaluation results are reported. We have conducted a series of experiments to evaluate our proposed technique and methodology. Our proposed technique is particularly valuable in building service search engines oriented to small- to middle-scale service repositories This paper reports our continuous efforts on semantic services discovery, extracting fragmental semantic data to support services discovery. Specially, we aim to explore an international standard-compatible approach to annotate and classify services on the service registry side. Our basic hypothesis is that, much knowledge is hidden in the service repositories and can be leveraged to enable and facilitate services discovery.