Intelligent hypertext for video selection: A design approach

Abstract

In this paper, we describe our project DemoMedia, a software demonstrator that combines hypertext and recommender functionality in the context of video acquisition or use. DemoMedia fills the gap that exists in today's video platforms which include recommender functionalities, but only trivial support for users to structure information. Thus, users are forced to write down notes from or about videos (needed for various reasons) on additional media, such as paper. This opens a media gap between video platform and note-taking or communication to others. DemoMedia becomes a note taking and communication tool for the user, as it offers a knowledge space on which users can freely arrange and associate information. Furthermore, its intelligent parsers compute relations that are implicitly expressed and queries knowledge bases for relevant information or related videos. Those get positioned on the space in a semantically meaningful way. DemoMedia and the underlying component-based open hypermedia system Mother combine both the machine's capability of extracting knowledge from huge amounts of data and the human capability of sensemaking, intuition, and creativity.