집단지성을 이용한 동영상 웹 콘텐츠의 구조

Abstract

Recently, KDD(Knowledge discovery in database) which discover knowledge by searching numerous information achieve great effects in various fields. However, utilizing distributed web contents into knowledge is not easy and especially video contents have structural limit which is hard to search and utilize sufficiently. To make use of video contents fully, its basic structure has to be improved. We proposed an architecture of video web contents using collective intelligence based on web platform which enlarged user's participation and range of demand in order to overcome its unstructured limit and lack of metadata in this paper. The segmentation of video contents using folksonomy convert to multi-dimentional structure with usefulness value for this architecture. And the video contents which is divided into segments will be used direct searching and regeneration to segments sets with same meaning. The usefulness value can be use to range search and multi dimensional structure can be approach to semantic search. We demonstrate metadata of video contents was much enlarged by folksonomy and was divided into segments having plenty of meanings in experimental results. Consequently, suggested the architecture of video web contents have advantages of effective use by special quality of collective intelligence.