delta's Introduction

DELTA: Description Logics with Transformers

Code to produce ALCQ-based knowledge bases as described in our paper: Transformers in the Service of Description Logic-based Contexts. The produced dataset DELTA_D of 384K examples is included, and the model DELTA_M that has been trained on DELTA_D can be accessed through huggingface.

The dataset generation process is illustrated below.

Knowledge Base generation

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).

Installation

This project requires Python 3 and several libraries, which can be installed with pip:

pip3 install -r requirements.txt

Usage

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.

Citation

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}
}