E-Learning platforms are evolving from monolithic applications with a rigid structure that did not allowed for the exchange of tools or components to applications incorporating service orientation concepts as well as facilitating the dynamic discovery and assembling of e-learning services. Accordingly, the usage of support materials to provide additional guidance to students facilitates the comprehension of learning tasks. Wikipedia is one of the richest sources of human knowledge, encompassing a vast range of topics of all kinds of information, and content, which is in constant change due to its collaborative dynamic nature. The Wikipedia Miner provides a code that can parse a given document identifying main topics and link them to corresponding articles or short definitions from the Wikipedia content. In this paper, we discuss the realization of a reusable Wikipedia Miner service for the e-Learning Computational Cloud (eLC2) Platform designed with the J2EE technology and Service-Oriented (V-MVC) model excluding a direct link between the Model and the View. This allows enhancing the Controller as a middleware, removing the dependency and acting as a single point of contact. In the V-MVC design pattern, the Controller is modeled by the compound design pattern of the Enterprise Service Bus (ESB) supporting higher privacy of the business logic and higher re-usability Architecture standards. The eLC2 is also based on an original Virtual Model-View-Controller of application components. In this framework, Wikipedia Miner services were prototyped as an Application Engine that wraps the logic of the Wikipedia Miner API in order to re-use it for different types of applications. Particularly, we are focusing on two applications in order to demonstrate the usability of the proposed approach. The first application is the WikiGloss tool, which is based on a glossing approach to help learners of English-as-second-language with an extensive reading task. The second application is an Intelligent Hints service for a Task Management Environment which provides explanatory links from relevant Wikipedia articles related to topics of the e-Learning task. This allows re-use of the same problems in different task type modes such as lectures, exercises, and quizzes.