Code and Data for the paper: "OntoZSL: Ontology-enhanced Zero-shot Learning". Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Jeff Z. Pan, Zhiquan Ye, Huajun Chen and others. The Web Conference (WWW) 2021 Research Track.
In this work, we propose to utilize ontology and generative adversarial network to deal with the zero-shot learning problems in image classification and KG completion.
| Dataset | # Classes (Total/Seen/Unseen) | # Ontology Schema (Triples/Concepts/Properties) |
|---|---|---|
| AwA | 50 / 40 / 10 | 1,256 / 180 / 12 |
| ImNet-A | 80 / 28 / 52 | 563 / 227 / 19 |
| ImNet-O | 35 / 10 / 25 | 222 / 115 / 8 |
| Dataset | # Relations (Total/Train/Val/Test) | # Ontology Schema (Triples/Concepts/Properties) |
|---|---|---|
| NELL-ZS | 139 / 10 / 32 | 3,055 / 1,186/4 |
| Wikidata-ZS | 469 / 20 / 48 | 10,399 / 3,491/8 |
python 3.5PyTorch >= 1.5.0
You need to download pretrained Glove word embedding dictionary, uncompress it and put all files to the folder data/glove/.
Download public image features and dataset split for AwA2, uncompress it and put the files in AWA2 folder to our folder data/AwA2/.
Download the image features and the word embeddings of ImageNet classes as well as their splits from here and put them to the folder data/ImageNet/.
You can download these two datasets from here and put them to the corresponding data folder.
The first thing you need to do is to train the text-aware ontology encoder using the code in the folder code/OntoEncoder, you can get more details at code/OntoEncoder/README.md.
Secondly, with well-trained ontology embedding, you can take it as the input of generative model, see the codes in the folders code/IMGC and code/KGC. The running commands are listed in the corresponding README.md files.
Note: you can skip the first step if you just want to use the ontology embedding we learned, the files are provided in the corresponding directories.
If you find this code useful, please consider citing the following paper.
@inproceedings{geng2021ontozsl,
author = {Yuxia Geng and
Jiaoyan Chen and
Zhuo Chen and
Jeff Z. Pan and
Zhiquan Ye and
Zonggang Yuan and
Yantao Jia and
Huajun Chen},
editor = {Jure Leskovec and
Marko Grobelnik and
Marc Najork and
Jie Tang and
Leila Zia},
title = {OntoZSL: Ontology-enhanced Zero-shot Learning},
booktitle = {{WWW} '21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia,
April 19-23, 2021},
pages = {3325--3336},
publisher = {{ACM} / {IW3C2}},
year = {2021},
url = {https://doi.org/10.1145/3442381.3450042},
doi = {10.1145/3442381.3450042},
timestamp = {Thu, 14 Oct 2021 10:04:23 +0200},
biburl = {https://dblp.org/rec/conf/www/GengC0PYYJC21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
We refer to the code of LisGAN and ZSGAN. Thanks for their contributions.