Entity Alignment is the task of linking entities with the same real-world identity from different knowledge graphs. EVA is a set of algorithms that leverage images in knowledge graphs for facilitating Entity Alignment.

This repo holds code for reproducing models presented in our paper: Visual Pivoting for (Unsupervised) Entity Alignment [arxiv][aaai] at AAAI 2021.

Download the used data (DBP15k, DWY15 along with precomputed features) from dropbox or BaiduDisk (code: dhya) (1.3GB after unzipping) and place under data/.

Original sources of DBP15k and DWY15k:

• DBP15k
• DWY15k

[optional] The raw images of entities appeared in DBP15k and DWY15k can be downloaded from dropbox (108GB after unzipping). All images are saved as title-image pairs in dictionaries and can be accessed with the following code:

import pickle
zh_images = pickle.load(open("eva_image_resources/dbp15k/zh_dbp15k_link_img_dict_full.pkl",'rb'))
print(en_images["http://zh.dbpedia.org/resource/香港有線電視"].size)

We use the DWY15k dataset as an example (files not used in experiments are omitted).

data/DWY_data/
├── dwy15k_dense_sf_vec.npy: surface form vectors encoded by fastText (dense split)
├── dwy15k_norm_sf_vec.npy: surface form vectors encoded by fastText (normal split)
├── dbp_wd_15k_V1/: normal split
│   ├── mapping/
│   │   ├── 0_3/: the third split (used across all experiments)
│   │   │   ├── ent_ids_1: mapping between entity names and ids for graph 1
│   │   │   ├── ent_ids_2: mapping between entity names and ids for graph 2
│   │   │   ├── rel_ids_1: mapping between relation names and ids for graph 1
│   │   │   ├── rel_ids_2: mapping between relation names and ids for graph 2
│   │   │   ├── ill_ent_ids: inter-lingual links (specified by ids)
│   │   │   ├── triples_1: a list of tuples in the form of (head, relation, tail) for graph 1 (specified by ids)
│   │   │   ├── triples_2: a list of tuples in the form of (head, relation, tail) for graph 2 (specified by ids)
│   │   │   ├── ...
│   │   ├── ...
│   ├── ...
├── dbp_wd_15k_V2/: dense split
│   ├── ...
data/pkls/
├── dbpedia_wikidata_15k_norm_GA_id_img_feature_dict.pkl: mapping between entity names to image features for DWY15k (normal)
│   ├── ...

The code is tested with python 3.7 and torch 1.7.0.

Run the full model on DBP15k:

./run_dbp15k.sh 0 2020 fr_en

where 0 specifies the GPU device, 2020 is a random seed and fr_en sets the language pair.

Similarly, you can run the full model on DWY15k:

./run_dwy15k.sh 0 2020 1

where the first two args are the same as before, the third specifies where using the normal (1) or dense (2) split.

To run without iterative learning:

./run_dbp15k_no_il.sh 0 2020 fr_en
./run_dwy15k_no_il.sh 0 2020 1

To run the unsupervised setting on DBP15k:

./run_dbp15k_unsup.sh 0 2020 fr_en

Our codes are modified from KECG. We appreciate the authors for making KECG open-sourced.

@inproceedings{liu2021visual,
  title={Visual Pivoting for (Unsupervised) Entity Alignment},
  author={Liu, Fangyu and Chen, Muhao and Roth, Dan and Collier, Nigel},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={5},
  pages={4257--4266},
  year={2021}
}

EVA is MIT licensed. See the LICENSE file for details.