Exploring Wikipedia with Hōpara

Abstract

Anyone who has browsed Wikipedia has likely experienced the feeling of being happily lost, browsing from one interesting topic to the next and encountering information that they would never have searched for explicitly. With some 3M articles and 70M links, Wikipedia represents an extreme example of large-scale hypertext. We consider it to be a rich and challenging platform for investigating navigation and disorientation in large interconnected information spaces. This demonstration showcases Hōpara, a new search engine that aims to make Wikipedia and its underlying link structure easier to explore. It works on top of the encyclopedia’s existing link structure, abstracting away from document content and allowing users to navigate the resource at a higher level. It utilizes semantic relatedness measures to emphasize articles and connections that are most likely to be of interest, visualization to expose the structure of how the available information is organized, and lightweight information extraction to explain itself.