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mOKB6: A Multilingual Open Knowledge Base Benchmark

We release a dataset called mOKB6 for multilingual open knowledge base completion task. mOKB6 has 42K facts in six languages: English, Hindi, Telugu, Spanish, Portuguese, and Chinese.

We also provide the code of our baseline knowledge graph embedding model, which is adapted from the publicly available official code repository (link) of Wang et al., 2022.

This work appeared at the ACL 2023 (main) conference and more details can be found in the paper (paper link). Checkout the video (link) or poster (link) presentation for a brief overview.

mOKB6 Dataset

The ./mokb6/mono/ folder contains the mOKB6 dataset, containing six monolingual open KBs in six languages:

  1. English Open KB inside ./mokb6/mono/mono_en
  2. Hindi Open KB inside ./mokb6/mono/mono_hi
  3. Telugu Open KB inside ./mokb6/mono/mono_te
  4. Spanish Open KB inside ./mokb6/mono/mono_es
  5. Portuguese Open KB inside ./mokb6/mono/mono_pt
  6. Chinese Open KB inside ./mokb6/mono/mono_zh

Each monolingual Open KB's folder contains three files: train.txt, valid.txt, and test.txt. These files are the train-dev-test splits of the respective language's Open KB, which contain tab-separated Open IE triples of the form (subject, relation, object).

For reproducibility of our results, we provide the translated Open KB facts. Thus, for each baseline given in Table 3 in the paper, we provide the corresponding dataset inside ./mokb6/ folder. For e.g., our best baseline (for all languages except English) called Union+Trans is trained using data contained in ./mokb6/union+trans/ for the 5 languages (./mokb6/union+trans/union+trans_en2hi/ for Hindi). Whereas the best performing baseline for English called Union can be reproduced using data contained in ./mokb6/union/.

Model

We benchmark Knowledge Graph Embedding (KGE) models from available repositories from CaRe, GRU-ConvE, and SimKGC on mOKB6 dataset.

We provide the code of SimKGC model (adapted from Wang et al., 2022) as it showed the best performance when compared with the other KGE models. We use it (with mBERT initialization) to report the baselines (Table 3 in the paper) and to do an empirical study of the task (e.g., Figure 3 in the paper).

How to Run

Here, we provide the commands to reproduce the scores for Union+Trans (given in Table 3 in the paper).

Requirements

  • python>=3.7
  • torch>=1.6 (for mixed precision training)
  • transformers>=4.15

All experiments are run with a single A100 (40GB) GPU.

Installation

conda create --name mokb python=3.7
conda activate mokb
pip install -r requirements.txt

Preprocessing the dataset

Create data directory for saving preprocessed data using mkdir data.

First, run the below command to process the data of monolingual Open KBs. This is necessary and must to do to have the correct evaluation of any model, i.e., a given model will be evaluated on each language's Open KB (which will not include any translated facts).

sh sh_preprocess_mono_okbs.sh

Then, set the environment variables baseline_name and baseline_data to the name of baseline of interest, and to its corresponding data's path, respectively.

baseline_data=./mokb6/union+trans/union+trans_en2hi
baseline_name=union+trans_en2hi

Run the following commands to preprocess the data and store it in ./data/${baseline_name}/ folder.

python convert_format_mokb.py --train ${baseline_data}/train.txt   --val ${baseline_data}/valid.txt --test ${baseline_data}/test.txt  --out_dir ./data/${baseline_name}

python3 preprocess.py --train-path ./data/${baseline_name}/train.txt --valid-path ./data/${baseline_name}/valid.txt --test-path ./data/${baseline_name}/test.txt --task mopenkb

Training

Set the environment variable batch_size as per the baseline (e.g., 128 for Mono baseline for all languages except English, and 256 for the remaining baselines).

batch_size=256

Run the below command to train SimKGC (mBERT) and store its best checkpoint in ./checkpoint/${baseline_name}/ folder.

python3 main.py --model-dir ./checkpoint/${baseline_name} --pretrained-model bert-base-multilingual-cased --pooling mean --lr 3e-5 --train-path ./data/${baseline_name}/train.txt.json  --valid-path ./data/${baseline_name}/valid.txt.json  --task mopenkb --batch-size ${batch_size} --print-freq 20 --additive-margin 0.02 --use-amp --use-self-negative --finetune-t --pre-batch 0 --epochs 100 --workers 3 --max-to-keep 0 --patience 10 --seed 2022

Evaluating

To evaluate the model on a given language's Open KB's testset, say hi, set the language variable

language=hi

Then, evaluate the model checkpoint ./checkpoint/${baseline_name}/model_best.mdl using the below command.

python3 evaluate.py --task mopenkb --pretrained-model bert-base-multilingual-cased --is-test --eval-model-path ./checkpoint/${baseline_name}/model_best.mdl --train-path data/mono_${language}/train.txt.json  --valid-path data/mono_${language}/test.txt.json

Cite

If you use or extend our work, please cite it:

@inproceedings{mittal-etal-2023-mokb6,
    title = "m{OKB}6: {A} {M}ultilingual {O}pen {K}nowledge {B}ase {C}ompletion {B}enchmark",
    author = "Mittal, Shubham  and
      Kolluru, Keshav  and
      Chakrabarti, Soumen  and
      -, Mausam",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.19",
    pages = "201--214",
}