A Prompt Learning Framework for Knowledge Graph Representation Learning and Application

A Prompt Learning Framework for Knowledge Graph  with Pre-trained Language Models across tasks (predictive and generative), and modalities (language, image, vision + language, etc.)

Correlative Toolkit:

DeepKE:A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Graph Construction

Paper: PromptKG: A Prompt Learning Framework for Knowledge Graph Representation Learning and Application

Create the conda env.

conda create -n promptkg python=3.8

Install the dependence.

pip install -r requirements.txt

Install our preprocessed datasets and put them into the dataset folder.

We provide four tasks in our toolkit as Knowledgeg Graph Completion (KGC), Question Answering (QA), Recomandation (REC) and LAnguage Model Analysis (LAMA).

• KGC is our basic task to the knowledge graph embedding and evaluate the ability of the models. ** You can run the script under kgc folder to train the model and get the KG embeddings (take simkgc as example).

  bash ./scripts/kgc/simkgc.sh

• For QA task, you can run the script files under metaqa. ** We suggest you use generative model to solve the QA task as below:

  bash ./scripts/metaqa/run.sh

• For REC task, you need to firstly get the KG embeddings and then train the rec system models. ** use two-stage scripts below:

  bash ./scripts/kgrec/pretrain_item.sh
  bash ./scripts/kgrec/ml20m.sh

• For LAMA task, you can use the files under lama. ** We provide BERT and RoBERTa PLMs to evaluate their performance and with our KG embeddings (plet).

  bash ./scripts/lama/lama_roberta.sh

For each knowledge graph, we have 5 files.

• train.tsv, dev.tsv, test.tsv, list as (h, r, t) for entity id and relation id (start from 0).
• entity2text.txt, as (entity_id, entity description).
• relation2text.txt , as (relation_id, relation description).

For help or issues using the models, please submit a GitHub issue.

If you use the code, please cite the following paper:

@article{DBLP:journals/corr/abs-2210-00305,
  author    = {Xin Xie and
               Zhoubo Li and
               Xiaohan Wang and
               Shumin Deng and
               Feiyu Xiong and
               Huajun Chen and
               Ningyu Zhang},
  title     = {PromptKG: {A} Prompt Learning Framework for Knowledge Graph Representation
               Learning and Application},
  journal   = {CoRR},
  volume    = {abs/2210.00305},
  year      = {2022},
  url       = {https://doi.org/10.48550/arXiv.2210.00305},
  doi       = {10.48550/arXiv.2210.00305},
  eprinttype = {arXiv},
  eprint    = {2210.00305},
  timestamp = {Fri, 07 Oct 2022 15:24:59 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2210-00305.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

• OpenKE
• LibKGE
• CogKGE
• PyKEEN
• GraphVite
• Pykg2vec
• PyG
• CogDL
• NeuralKG