license: gpl-3.0
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Exploring and Verbalizing Academic Ideas by Concept Co-occurrence
https://github.com/xyjigsaw/Kiscovery
Evolving Concept Co-occurrence Graph
It is the official Evolving Concept Co-occurrence Graph dataset of paper Exploring and Verbalizing Academic Ideas by Concept Co-occurrence.
To train our model for temporal link prediction, we first collect 240 essential and common queries from 19 disciplines and one special topic (COVID-19). Then, we enter these queries into the paper database to fetch the most relevant papers between 2000 and 2021 with Elasticsearch, a modern text retrieval engine that stores and retrieves papers. Afterward, we use information extraction tools including AutoPhrase to identify concepts. Only high-quality concepts that appear in our database will be preserved. Finally, we construct 240 evolving concept co-occurrence graphs, each containing 22 snapshots according to the co-occurrence relationship. The statistics of the concept co-occurrence graphs are provided in Appendix I.
Download with git, and you should install git-lfs first
sudo apt-get install git-lfs
# OR
brew install git-lfs
git lfs install
git clone https://huggingface.co/datasets/Reacubeth/ConceptGraph
Citation
If you use our work in your research or publication, please cite us as follows:
@inproceedings{xu2023exploring,
title={Exploring and Verbalizing Academic Ideas by Concept Co-occurrence},
author={Xu, Yi and Sheng, Shuqian and Xue, Bo and Fu, Luoyi and Wang, Xinbing and Zhou, Chenghu},
booktitle={Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL)},
year={2023}
}
Please let us know if you have any questions or feedback. Thank you for your interest in our work!