LargeEA

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Source code for LargeEA: Aligning Entities for Large-scale Knowledge Graphs, VLDB 2022

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# LargeEA

Source code for LargeEA: Aligning Entities for Large-scale Knowledge Graphs

If there is any problem with reproduction, please create an issue in [The GitHub repo page](https://github.com/ZJU-DBL/LargeEA)

## Requirements

pytorch>=1.7.0

tensorflow>=2.4.1 (required for RREA)

faiss

transformers

datasketch[redis]

...

A full list of required packages is located in ``src/requirements.txt``

## Datasets 
The IDS benchmark is provided by [OpenEA](https://github.com/nju-websoft/OpenEA)

Our newly proposed benchmark DBP1M is available at [Google Drive](https://drive.google.com/file/d/15jeGD-6pVGlqI5jCn7KJfGIER6AeoQ-L/view?usp=sharing)

First download and unzip dataset files, place them to the project root folder:

unzip OpenEA_dataset_v1.1.zip
    unzip mkdata.zip

The __dataset__ (small for IDS15K, medium for IDS100K, large for DBP1M) and  __lang__ (fr or de) parameter controls which benchmark to use.
For example, in the ``src`` folder, setting dataset to small and lang to fr will run on OpenEA EN_FR_15K_V1 dataset.

## Run

Take __DBP1M(EN-FR)__ as an example:

Make sure the folder for results is created:

cd src/
    mkdir tmp4

### Name Channel

First get the BERT embeddings of all entities

python main.py --phase 1 --dataset large --lang fr

Then calculate TopK sims based on BERT:

python main.py --phase 2 --dataset large --lang fr

Finally the string-based similarity(this requires a redis server listening  localhost:6379):

python main.py --phase 3 --dataset large --lang fr

### Structure Channel

The structure channel uses result of name channel to get name-based seeds. Make sure run name channel first.

To run RREA model:

python main.py --phase 0 --dataset large --lang fr --model rrea --epoch 100

### Channel Fusion and Eval

python main.py --phase 4  --dataset large --lang fr

## Citation

Reference to cite when you use LargeEA in a research paper:

@article{largeEA,
      author    = {Congcong Ge and
                   Xiaoze Liu and
                   Lu Chen and
                   Baihua Zheng and
                   Yunjun Gao},
      title     = {LargeEA: Aligning Entities for Large-scale Knowledge Graphs},
      journal   = {{PVLDB}},
      volume    = {15},
      number    = {2},
      pages     = {237--245},
      year      = {2022}
    }

## Acknowledgements

We use the codes of 
[MRAEA](https://github.com/MaoXinn/MRAEA),
[RREA](https://github.com/MaoXinn/RREA), 
[GCN-Align](https://github.com/1049451037/GCN-Align),
[DGMC](https://github.com/rusty1s/deep-graph-matching-consensus),
[AttrGNN](https://github.com/thunlp/explore-and-evaluate),
[OpenEA](https://github.com/nju-websoft/OpenEA),
[EAKit](https://github.com/THU-KEG/EAKit),
[SimAlign](https://github.com/cisnlp/simalign).

We also provide the modified version of OpenEA in order to run experiments on RTX3090 GPU:[OpenEA-TF2](https://github.com/joker-xii/OpenEA-TF2)

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