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This repository contains the code for the paper "PreAdapter: Pre-training Language Models on Knowledge Graphs" at ISWC 2024

Dependencies

Experiments were run using python 3.8

Pip dependencies are listed in the requirements.txt file.

Running the code

The code can be run using the run.py script. Run arguments may be adjusted in the config.py file.

The following arguments are used for the specific models run in the paper:

RoBERTa: --backbone roberta --baseline one_qa_dragon

Adapter: --backbone roberta_adapter --baseline one_qa_dragon

K-Adapter: --backbone roberta_adapter --baseline one_qa_dragon

CapsKG: --backbone roberta_adapter --baseline ctr_kg_qa_dragon

PreAdapter: --backbone roberta_adapter --baseline ctr_kg_qa_dragon --build_adapter_mask

Data

The data used in the experiments are contained in the dat folder.

Templates

Used templates are contained within the specific dataset folders in templates.json.

KG

Available Knowledge Graphs (KGs) are:

  • ConceptNet CSQA
  • ConceptNet OBQA

QA

Available Question Answering (QA) datasets are:

  • CSQA
  • OBQA

Example usage:

python3 run.py --baseline ctr_kg_qa_dragon --backbone roberta_adapter --task qa_dragon --use_predefine_args --num_train_epochs 5 --max_seq_length=64 --build_adapter_mask --yaml_param_num 0 --output_dir "\my_output_directory" --auto_resume --reuse_data

Citation:

If you use this code in your experiments, please cite the following paper:

@inproceedings{omeliyanenko2024preadapter,
  title={PreAdapter: Pre-training Language Models on Knowledge Graphs},
  author={Omeliyanenko, Janna and Hotho, Andreas and Schl{\"o}r, Daniel},
  booktitle={International Semantic Web Conference},
  pages={210--226},
  year={2024},
  organization={Springer}
}