
## Homepage

Exploring and Verbalizing Academic Ideas by Concept Co-occurrence

[https://github.com/xyjigsaw/Kiscovery](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](https://github.com/shangjingbo1226/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

```bash
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!


license:gpl-3.0 region:us
{"license": "gpl-3.0"}
TAGS
#license-gpl-3.0 #region-us 


## Homepage

Exploring and Verbalizing Academic Ideas by Concept Co-occurrence

URL

## 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



If you use our work in your research or publication, please cite us as follows:



Please let us know if you have any questions or feedback. Thank you for your interest in our work!


## Homepage

Exploring and Verbalizing Academic Ideas by Concept Co-occurrence

URL ## 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



If you use our work in your research or publication, please cite us as follows:



Please let us know if you have any questions or feedback. Thank you for your interest in our work!
TAGS
#license-gpl-3.0 #region-us 
 ## Homepage

Exploring and Verbalizing Academic Ideas by Concept Co-occurrence

URL ## 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



If you use our work in your research or publication, please cite us as follows:



Please let us know if you have any questions or feedback. Thank you for your interest in our work!
passage: TAGS
#license-gpl-3.0 #region-us 
## Homepage

Exploring and Verbalizing Academic Ideas by Concept Co-occurrence

URL## 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



If you use our work in your research or publication, please cite us as follows:



Please let us know if you have any questions or feedback. Thank you for your interest in our work!