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GAKG is a multimodal Geoscience Academic Knowledge Graph (GAKG) framework by fusing papers' illustrations, text, and bibliometric data.

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<div style="text-align:center">
<img src="https://www.cdeng.net/gakg/logo.png" alt="gakg-logo" width="600"/>
<h2>GAKG: A Multimodal Geoscience Academic Knowledge Graph 😈</h2>
</div>

The **SPARQL Query Endpoint** of GAKG is [**gakg/sparql**](https://www.acekg.cn/sparql), with the **Graph IRI**: `https://www.acekg.cn/`.

# Overview

GAKG is a multimodal Geoscience Academic Knowledge Graph (GAKG) framework by fusing papers' illustrations, text, and bibliometric data. To our knowledge, GAKG is currently the largest and most comprehensive geoscience academic knowledge graph, consisting more than 68 million triples. Figure 1 shows the overview of GAKG. And if you want to explore the entire GAKG, view [https://gakg.acemap.info](https://gakg.acemap.info).

<div style="text-align:center">
<img src="https://www.cdeng.net/gakg/overview.png" alt="gakg-logo" width="800"/>
<h6>Figure 1. Overview of Multimodal GeoScience Academic Knowledge Graph (GAKG) </h6>
</div>

In order to better serve the data mining and knowledge discovery communities, GAKG preserves several datasets and GAKG-oriented resources.

<h2 id="sn">Community Detection</h2>

-  Statistics

|dataset|node|edge|num. lcc|clustering|triangle|daglongest|
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
|GPCN|842,121|16,034,510|1,450|0.0699|38,789,469|176|
|GPCN-lwcc|838,219|16,031,892|1|0.0701|38,789,345|176|
|GACN|860,280|5,381,861|32,609|0.690|43,502,542|15|
|GACN-lc|752,718|5,231,507|1|0.700|43,332,307|15|

- Baselines

The entire benchmarks also can be accessed from [google drive](https://drive.google.com/drive/folders/1Bfx7StqMXwEeDaH9GqSyWKe32iaP0ked?usp=sharing)

<h2 id="krl">Knowledge Representation Learning</h2>

-  Statistics

|Benchmark|Entities Type|Relations TypeS|Triples|
|:-:|:-:|:-:|:-:|
|GA16K|10|16,363|151,662|
|WN18|18|40,943|141,442|
|FB15K|1,345|14,951|483,142|

- Baselines

<h2 id="pkg">Paper Knowledge Extraction</h2>

The pipeline of Paper Knowledge Extraction is in folder `/code/paperknowledge/`

- BERT-based QA (`/code/paperknowledge/bertQA`)

- run single QA, with parameters: *train_batch_size，learning_rate*
    ```bash
    python run_squad.py --do_predict
    ```

- run batch QA, with parameters: *start file, end file, and year*
    ```bash
    CUDA_VISIBLE_DEVICES=1 python run_test.py 116 125 2015
    ```

- `/code/paperknowledeg/bertQA/model` can be download via [google drive](https://drive.google.com/drive/folders/1Map0STLr3ScCtqRJTawg_Vlu6xXzt17U?usp=sharing)

- Paper Knowledge Entities Extraction (`/code/paperknowledge/paperentity`)

- run
    ```bash
    python example.py
    ```

- `/code/paperknowledge/paperentity/dde` and related files can be download via [google drive](https://drive.google.com/drive/folders/1L9_CJtmvrzfaYIcpzyuGf4-2Wzu274jn?usp=sharing)

## Citation

Our [paper]([https://www.cdeng.net/files/papers/GAKG.pdf](https://dl.acm.org/doi/10.1145/3459637.3482003)) has been accepted as a resource paper at CIKM-2021 :
```bibtex
@inproceedings{deng2021gakg,
  title={GAKG: A Multimodal Geoscience Academic Knowledge Graph},
  author={Deng, Cheng and Jia, Yuting and Xu, Hui and Zhang, Chong and Tang, Jingyao and Fu, Luoyi and Zhang, Weinan and Zhang, Haisong and Wang, Xinbing and Zhou, Chenghu},
  booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
  pages={4445--4454},
  year={2021}
}
```

## System Introduction
We now introduce the GAKG platform:
[![GAKG Knowledge Navigation System](https://res.cloudinary.com/marcomontalbano/image/upload/v1668011655/video_to_markdown/images/youtube---f-trGxy9g0-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://youtu.be/-f-trGxy9g0 "GAKG Navigation System")

<!-- ## To-do

- [ ] ***Coming soon:*** Python Package gakg -->

[1]: https://iopscience.iop.org/article/10.1088/1742-5468/2008/10/P10008/meta
[2]: https://www.pnas.org/content/105/4/1118/
[3]: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.76.036106

[4]: https://icml.cc/2011/papers/438_icmlpaper.pdf
[5]: https://papers.nips.cc/paper/2013/file/1cecc7a77928ca8133fa24680a88d2f9-Paper.pdf
[6]: https://persagen.com/files/misc/wang2014knowledge.pdf
[7]: https://arxiv.org/pdf/1802.04868.pdf
[8]: https://arxiv.org/pdf/1902.10197.pdf

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