Code to produce ALCQ-based knowledge bases as described in our paper: Transformers in the Service of Description Logic-based Contexts. The produced dataset DELTAD of 384K examples is included, and the model DELTAM that has been trained on DELTAD can be accessed through huggingface.
The dataset generation process is illustrated below.
Given an ALCQ Probabilistic Context Free Grammar (PCFG) the data_generator.py will produce num_of_examples KBs with questions within the target reasoning depth (max_depth).
This project requires Python 3 and several libraries, which can be installed with pip:
pip3 install -r requirements.txt
python3 data_generator.py --grammar grammars_and_config/ALCQ_grammarsV1/ALCQGrammarL<0/1/2/3>.txt
--config-json grammars_and_config/config/D<1/2/3/5>_config.json
--num-of-examples <n_examples> --max-depth <1/2/3/4/5>
--output-jsonl <output-file-name>.jsonl
Where:
<grammar-file>is the path to a text file containing the ALCQ PCFG. Valid options are ALCQGrammarLi.txt for i in {0,1,2,3}.<config-file>is the path to a JSON file containing the configuration. Valid options are Dj_config.json for j in {0,1,2,3,5}.<n_examples>is the number of examples (KBs) to generate.<depth>is the target reasoning depth of the generated questions. Valid options are 1, .., 5.<output-file>is the name of the JSONL file to output the generated data.
If you use our code/dataset in your work please cite our paper:
@misc{poulis2024transformers,
title={Transformers in the Service of Description Logic-based Contexts},
author={Angelos Poulis and Eleni Tsalapati and Manolis Koubarakis},
year={2024},
eprint={2311.08941},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
If you encounter any problems or have questions about DELTA, please feel free to contact me at reverse("angelos") + "ps" + [at]"gmail.com".