Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia
- URL: http://arxiv.org/abs/2211.00732v3
- Date: Fri, 11 Aug 2023 04:00:59 GMT
- Title: Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia
- Authors: Haojie Pan, Zepeng Zhai, Yuzhou Zhang, Ruiji Fu, Ming Liu, Yangqiu
Song, Zhongyuan Wang and Bing Qin
- Abstract summary: Kuaipedia is a large-scale multi-modal encyclopedia consisting of items, aspects, and short videos lined to them.
It was extracted from billions of videos of Kuaishou, a well-known short-video platform in China.
- Score: 59.47639408597319
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Online encyclopedias, such as Wikipedia, have been well-developed and
researched in the last two decades. One can find any attributes or other
information of a wiki item on a wiki page edited by a community of volunteers.
However, the traditional text, images and tables can hardly express some
aspects of an wiki item. For example, when we talk about ``Shiba Inu'', one may
care more about ``How to feed it'' or ``How to train it not to protect its
food''. Currently, short-video platforms have become a hallmark in the online
world. Whether you're on TikTok, Instagram, Kuaishou, or YouTube Shorts,
short-video apps have changed how we consume and create content today. Except
for producing short videos for entertainment, we can find more and more authors
sharing insightful knowledge widely across all walks of life. These short
videos, 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), and
they can be systematically analyzed and organized like an online encyclopedia.
In this paper, we propose Kuaipedia, a large-scale multi-modal encyclopedia
consisting of items, aspects, and short videos lined to them, which was
extracted from billions of videos of Kuaishou (Kwai), a well-known short-video
platform in China. We first collected items from multiple sources and mined
user-centered aspects from millions of users' queries to build an item-aspect
tree. Then we propose a new task called ``multi-modal item-aspect linking'' as
an expansion of ``entity linking'' to link short videos into item-aspect pairs
and build the whole short-video encyclopedia. Intrinsic evaluations show that
our encyclopedia is of large scale and highly accurate. We also conduct
sufficient extrinsic experiments to show how Kuaipedia can help fundamental
applications such as entity typing and entity linking.
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