A Semi-Automated approach using Kanban to build taxonomies for Multimedia Contents

As of today, multimedia repositories have to manage more and more multimedia objects; this leads to the need for robust systems for taxonomy building, and, in turns, for well-suited metadata for specific domains. The knowledge-base must be dynamic, so to change with time, since new kinds of multimedia objects have to be retrieved with properly selected metadata. Automated systems are widely employed for repositories management, but many of them could be enhanced through the use of widespread methodologies that, in a collaborative context like that of taxonomy building, could be most useful. We propose a semi-automated system for taxonomy construction, based on Kanban driven approach to metadata selection and validation. The main goal is to improve the accuracy of automated systems, not capable of identifying all the useful metadata. This will be achieved through both top-down and bottom-up phases, aided by a validation process where Kanban is employed. The multimedia repository could be then fully embedded in Content Management Systems (CMS), or used through e-learning platforms.