This is the forked repository of ToG, that has been modified to serve as baseline for the DynamicKGQA project. The original repository was designed to work with Wikidata and Freebase, but we have made modifications to make it work with YAGO as well.

The original repo for ToG is Here.

• requirements.txt: Pip environment file.
• data/: Evaluation datasets. See data/README.md for details.
• CoT/: CoT methods. See CoT/README.md for details.
• eval/: Evaluation script. See eval/README.md for details.
• Freebase/: Freebase environment setting. See Freebase/README.md for details.
• Wikidata/: Wikidata environment setting. See Wikidata/README.md for details.
• tools/: Common tools used in ToG. See tools/README.md for details.
• ToG/: Source codes.
  • client.py: Pre-defined Wikidata APIs, copy from Wikidata/.
  • server_urls.txt: Wikidata server urls, copy from Wikidata/.
  • main_freebase.py: The main file of ToG where Freebase as KG source. See README.md for details.
  • main_wiki.py: Same as above but using Wikidata as KG source. See README.md for details.
  • prompt_list_freebase.py: The Freebase-oriented prompts for the ToG to pruning, reasoning and generating.
  • freebase_func.py: All the functions used in main_freebase.py.
  • wiki_func.py: All the functions used in main_wiki.py.
  • main_yago.py: The main file of ToG where YAGO as KG source. See README.md for details.
  • yago_func.py: All the functions used in main_yago.py.
  • prompt_list.py: The YAGO-oriented prompts for the ToG to pruning, reasoning and generating.
  • utils.py: All the functions used in ToG.

Before running ToG, please ensure that you have successfully installed Yago, Freebase or Wikidata on your local machine. The comprehensive installation instructions and necessary configuration details can be found in the README.md file located within the respective folder.

The required libraries for running ToG can be found in requirements.txt.

When using the Wikidata service, copy the client.py and server_urls.txt files from the Wikidata directory into the ToG folder.

See ToG/ README.md

Upon obtaining the result file, such as ToG_cwq.jsonl, you should using the jsonl2json.py script from the tools directory to convert the ToG_cwq.jsonl to ToG_cwq.json. Then, evaluate using the script in the eval folder (see README.md in eval folder).

Since this work is intended to be a baseline for the DynamicKGQA project, please cite our work as follows: TBD

@article{dammu2025dynamic,
  title={Dynamic-KGQA: A Scalable Framework for Generating Adaptive Question Answering Datasets},
  author={Dammu, Preetam Prabhu Srikar and Naidu, Himanshu and Shah, Chirag},
  journal={arXiv preprint arXiv:2503.05049},
  year={2025}
}

Also please cite the origin paper on ToG as following:

@misc{sun2023thinkongraph,
      title={Think-on-Graph: Deep and Responsible Reasoning of Large Language Model with Knowledge Graph}, 
      author={Jiashuo Sun and Chengjin Xu and Lumingyuan Tang and Saizhuo Wang and Chen Lin and Yeyun Gong and Heung-Yeung Shum and Jian Guo},
      year={2023},
      eprint={2307.07697},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

This project is licensed under the MIT License - see the LICENSE file for details.