BLINK is an Entity Linking python library that uses Wikipedia as the target knowledge base.
The process of linking entities to Wikipedia is also known as Wikification.
The BLINK architecture is described in the following paper:
@inproceedings{wu2019zero,
title={Zero-shot Entity Linking with Dense Entity Retrieval},
author={Ledell Wu, Fabio Petroni, Martin Josifoski, Sebastian Riedel, Luke Zettlemoyer},
booktitle={arXiv:1911.03814},
year={2019}
}https://arxiv.org/pdf/1911.03814.pdf
In a nutshell, BLINK uses a two stage approach for entity linking, based on fine-tuned BERT architectures. In the first stage, BLINK performs retrieval in a dense space defined by a bi-encoder that independently embeds the mention context and the entity descriptions. Each candidate is then examined more carefully with a cross-encoder, that concatenates the mention and entity text. BLINK achieves state-of-the-art results on multiple datasets.
(optional) It might be a good idea to use a separate conda environment. It can be created by running:
conda create -n blink37 -y python=3.7 && conda activate blink37
pip install -r requirements.txtThe BLINK pretrained models can be downloaded using the following script:
chmod +x download_models.sh
./download_models.shWe additionally provide a FAISS indexer in BLINK, which enables efficient exact/approximate retrieval for biencoder model.
To build and save FAISS (exact search) index yourself, run
python blink/build_faiss_index.py --output_path models/faiss_flat_index.pkl
A quick way to explore the BLINK linking capabilities is through the main_dense interactive script. BLINK uses Flair for Named Entity Recognition (NER) to obtain entity mentions from input text, then run entity linking.
python blink/main_dense.py -iFast mode: in the fast mode the model only uses the bi-encoder, which is much faster (accuracy drops slightly, see details in "Benchmarking BLINK" section).
python blink/main_dense.py -i --fastTo run BLINK with saved FAISS index, run:
python blink/main_dense.py --faiss_index flat --index_path models/faiss_flat_index.pklor
python blink/main_dense.py --faiss_index hnsw --index_path models/faiss_hnsw_index.pklExample:
Bert and Ernie are two Muppets who appear together in numerous skits on the popular children's television show of the United States, Sesame Street.Output:
Note: passing --show_url argument will show the Wikipedia url of each entity. The id number displayed corresponds to the order of entities in the entity.jsonl file downloaded from ./download_models.sh (starts from 0). The entity.jsonl file contains information of one entity per row (includes Wikipedia url, title, text, etc.).
pip install -e git+git@github.com:facebookresearch/BLINK#egg=BLINKimport blink.main_dense as main_dense
import argparse
models_path = "models/" # the path where you stored the BLINK models
config = {
"test_entities": null,
"test_mentions": null,
"interactive": false,
"biencoder_model": models_path+"biencoder_wiki_large.bin",
"biencoder_config": models_path+"biencoder_wiki_large.json",
"entity_catalogue": models_path+"entity.jsonl",
"entity_encoding": models_path+"all_entities_large.t7",
"crossencoder_model": models_path+"crossencoder_wiki_large.bin",
"crossencoder_config": models_path+"crossencoder_wiki_large.json",
"fast": false, # set this to be true if speed is a concern
"output_path": "logs/" # logging directory
}
args = argparse.Namespace(**config)
models = main_dense.load_models(args, logger=None)
data_to_link = [ {
"id": 0,
"label": "unknown",
"label_id": -1,
"context_left": "".lower(),
"mention": "Shakespeare".lower(),
"context_right": "'s account of the Roman general Julius Caesar's murder by his friend Brutus is a meditation on duty.".lower(),
},
{
"id": 1,
"label": "unknown",
"label_id": -1,
"context_left": "Shakespeare's account of the Roman general".lower(),
"mention": "Julius Caesar".lower(),
"context_right": "'s murder by his friend Brutus is a meditation on duty.".lower(),
}
]
_, _, _, _, _, predictions, scores, = main_dense.run(args, logger=None, *models, test_data=data_to_link)
We provide scripts to benchmark BLINK against popular Entity Linking datasets. Note that our scripts evaluate BLINK in a full Wikipedia setting, that is, the BLINK entity library contains all Wikipedia pages.
To benchmark BLINK run the following commands:
./scripts/get_train_and_benchmark_data.sh
python scripts/create_BLINK_benchmark_data.py
python blink/run_benchmark.pyThe following table summarizes the performance of BLINK for the considered datasets.
| dataset | biencoder accuracy (fast mode) | biencoder recall@10 | biencoder recall@30 | biencoder recall@100 | crossencoder normalized accuracy | overall unnormalized accuracy | support |
|---|---|---|---|---|---|---|---|
| AIDA-YAGO2 testa | 0.8145 | 0.9425 | 0.9639 | 0.9826 | 0.8700 | 0.8212 | 4766 |
| AIDA-YAGO2 testb | 0.7951 | 0.9238 | 0.9487 | 0.9663 | 0.8669 | 0.8027 | 4446 |
| ACE 2004 | 0.8443 | 0.9795 | 0.9836 | 0.9836 | 0.8870 | 0.8689 | 244 |
| aquaint | 0.8662 | 0.9618 | 0.9765 | 0.9897 | 0.8889 | 0.8588 | 680 |
| clueweb - WNED-CWEB (CWEB) | 0.6747 | 0.8223 | 0.8609 | 0.8868 | 0.826 | 0.6825 | 10491 |
| msnbc | 0.8428 | 0.9303 | 0.9546 | 0.9676 | 0.9031 | 0.8509 | 617 |
| wikipedia - WNED-WIKI (WIKI) | 0.7976 | 0.9347 | 0.9546 | 0.9776 | 0.8609 | 0.8067 | 6383 |
| TAC-KBP 20101 | 0.8898 | 0.9549 | 0.9706 | 0.9843 | 0.9517 | 0.9087 | 1019 |
1 Licensed dataset available here.
The BLINK knowledge base (entity library) is based on the 2019/08/01 Wikipedia dump, downloadable in its raw format from http://dl.fbaipublicfiles.com/BLINK/enwiki-pages-articles.xml.bz2
The first version of BLINK uses an Apache Solr based Information Retrieval system in combination with a BERT based cross-encoder. This IR-based version is now deprecated since it's outperformed by the current BLINK architecture. If you are interested in the old version, please refer to this README.
BLINK is currently maintained by Ledell Wu, Fabio Petroni and Martin Josifoski.
If the module cannot be found, preface the python command with PYTHONPATH=.
BLINK is MIT licensed. See the LICENSE file for details.