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Online encyclopedias, such as Wikipedia, have been well-developed andresearched in the last two decades. One can find any attributes or otherinformation of a wiki item on a wiki page edited by a community of volunteers.However, the traditional text, images and tables can hardly express someaspects of an wiki item. For example, when we talk about ``Shiba Inu'', one maycare more about ``How to feed it'' or ``How to train it not to protect itsfood''. Currently, short-video platforms have become a hallmark in the onlineworld. Whether you're on TikTok, Instagram, Kuaishou, or YouTube Shorts,short-video apps have changed how we consume and create content today. Exceptfor producing short videos for entertainment, we can find more and more authorssharing insightful knowledge widely across all walks of life. These shortvideos, which we call knowledge videos, can easily express any aspects (e.g.hair or how-to-feed) consumers want to know about an item (e.g. Shiba Inu), andthey can be systematically analyzed and organized like an online encyclopedia.In this paper, we propose Kuaipedia, a large-scale multi-modal encyclopediaconsisting of items, aspects, and short videos lined to them, which wasextracted from billions of videos of Kuaishou (Kwai), a well-known short-videoplatform in China. We first collected items from multiple sources and mineduser-centered aspects from millions of users' queries to build an item-aspecttree. Then we propose a new task called ``multi-modal item-aspect linking'' asan expansion of ``entity linking'' to link short videos into item-aspect pairsand build the whole short-video encyclopedia. Intrinsic evaluations show thatour encyclopedia is of large scale and highly accurate. We also conductsufficient extrinsic experiments to show how Kuaipedia can help fundamentalapplications such as entity typing and entity linking.

Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia