metadata
license: cc-by-nc-sa-4.0
language:
- zh
Kuaipedia is developed by KwaiKEG, collaborating with HIT and HKUST. It is the world's first large-scale multi-modal short-video encyclopedia where the primitive units are items, aspects, and short videos.
- Items is a set of entities and concepts, such as Shiba Inu, Moon and Galileo Galilei, which can be edited at one Wikipedia page. An item may have a title, a subtitle, a summary, attributes, and other detailed information of the item.
- Aspects is a set of keywords or keyphrases attached to items. Those keywords are used to describe specific aspects of the item. For example, "selection", "food-protecting", "color" of item Shiba Inu, or "formation", "surface conditions", "how-to-draw" of item Moon.
- Videos is a set of short-videos whose duration may not exceed 5 minutes. In this paper, we only focus on knowledge videos we detected, Where we follow OECD to define knowledge as:
- Know-what refers to knowledge about facts. E.g. How many people live in New York?
- Know-why refers to scientific knowledge of the principles and laws of nature. E.g. Why does the earth revolve around the sun?
- Know-how refers to skills or the capability to do something. E.g. How to cook bacon in the oven.
Please refer to the paper for more details.
Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia [Manuscript]
Data
Statistics
Full Dump | Subset Dump | |
---|---|---|
#Items | > 26 million | 51,702 |
#Aspects | > 2.5 million | 1,074,539 |
#Videos | > 200 million | 769,096 |
The comparative results with the baseline models are as follows:
Model | Item P | Item R | Item-Aspect P | Item-Aspect R |
---|---|---|---|---|
Random | 87.7 | 49.8 | 36.4 | 49.6 |
LR | 90.4 | 68.3 | 55.1 | 2.7 |
T5-small | 93.7 | 76.1 | 79.3 | 58.5 |
BERT-base | 94.3 | 77.8 | 81.5 | 62.7 |
GPT-3.5 | 90.5 | 86.4 | 41.8 | 95.7 |
Ours | 94.7 | 79.7 | 83.0 | 65.7 |
Feel free to explore and utilize this valuable dataset for your research and projects.
Reference
@article{Kuaipedia22,
author = {Haojie Pan and
Zepeng Zhai and
Yuzhou Zhang and
Ruiji Fu and
Ming Liu and
Yangqiu Song and
Zhongyuan Wang and
Bing Qin
},
title = {{Kuaipedia:} a Large-scale Multi-modal Short-video Encyclopedia},
journal = {CoRR},
volume = {abs/2211.00732},
year = {2022}
}