Extraction of the Related Parts of Documents based on Sample Application for Platform Learning

Abstract

As well as understanding the architecture of a platform, knowledge about the features of the platform is required in the programming with the platform. In this paper, we present a method to support learning the knowledge about features. The knowledge is generally dispersed in multiple knowledge sources hence we organize and systematize the knowledge sources by a unit named learning items. Learner hereby obtains knowledge for learning items in just proportion. Next we propose a method to automatically extract an appropriate amount of the learning items for one study using filtration of sample application. Knowledge is enforced with facts found in the application. In the feasibility study, we organized the knowledge of the Google Android software platform for cellular phones, to 328 learning items for 94 kinds of major APIs. We developed a prototype platform-learning support system based on our method and applied it to seven sample applications of Android. In the result, an amount of files in each document are reduced to about 3.5%.