Linking Surface Facts to Large-Scale Knowledge Graphs

This repository contains the code & data of the paper Linking Surface Facts to Large-Scale Knowledge Graphs, published at EMNLP 2023.

Dependencies

Using miniconda, the virtual environment including all dependencies should be easily reproduced as conda env create -f environment.yml.

Model Zoo

The models that will be released soon are:

Model	Dataset	Download
OIE pre-ranker	REBEL	Link
OIE pre-ranker + Context	REBEL	Link
OIE-Fact re-ranker	REBEL	Link
OIE pre-ranker	SynthIE	Link

note that we are currently waiting for an approval of our legal team before the release.

Downloading and preparing the data

In order to be able to run experiments - train new models, or peform inference with existing ones - the data has to be either downloaded or re-created.

Wikidata Dump

Downloading and processing Wikidata takes a while, and for that reason we release a .json file dump of Wikidata. After cloning the repository, you can obtain the processed version of Wikidata inside the data/wikidata directory as:

conda install -c conda-forge git-lfs
git lfs install
git lfs pull

Benchmark Datasets

The datasets are released with the same license as this repository. You can find our data here.

Wikidata Knowledge Graph embeddings

The Wikidata Knowledge Graph embeddings are needed for inference, and will be released with the models.

Inference

Assuming the virutal environment is activated (conda activate kg-grounding), the models are downloaded (e.g., the OIE pre-ranker trained on REBEL, and the OIE-Fact re-ranker) in the experiments/ directory, the datasets are either (re-)created or downloaded, and Wikidata is downloaded, inference can be run as:

python src/inference-slot-linking.py --slot_linking_experiment_path "experiments/preranker-rebel-context/" --fact_reranking_experiment_path "experiments/reranker-rebel/" --reranker_k 2 --opts DEVICE "cuda:0" BATCH_SIZE 128 NUM_WORKERS 8 TEST_DATASET_PATH "data/datasets/val_inductive.json" INDEX_PATH "experiments/preranker-rebel-context/kg-index"

where INDEX_PATH indicates whether we perform OIE linking on a benchmark-restricted-KG-index (kg-index), or large-scale-KG-index (full-kg-index).

Training

Training new models also assumes that the environment is activated, and that the datasets and Wikidata are downloaded. Then, you can train (e.g., an OIE pre-ranker) model as:

python src/train-slot-linking.py --config_path "configs/preranker.yaml"

In order to modify any of the config.yaml values provide --opts ... after the config_path as: --opts BATCH_SIZE 128 NUM_WORKERS 12 ....

License

Please see the license file.

Citation

If you use our work or resources for your research, please cite the following paper:

@article{radevski2023linking,
  title={Linking surface facts to large-scale knowledge graphs},
  author={Radevski, Gorjan and Gashteovski, Kiril and Hung, Chia-Chien and Lawrence, Carolin and Glava{\v{s}}, Goran},
  journal={arXiv preprint arXiv:2310.14909},
  year={2023}
}