A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings.

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Overview • Installation • How To Use • Paper • Medium • Citation • Others

Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. Text-based KG embeddings can represent entities by encoding descriptions with pre-trained language models, but no open-sourced library is specifically designed for KGs with PLMs at present.

We present LambdaKG, a library for KGE that equips with many pre-trained language models (e.g., BERT, BART, T5, GPT-3), and supports various tasks (e.g., knowledge graph completion, question answering, recommendation, and knowledge probing).

LambdaKG is now publicly open-sourced, with a demo video and long-term maintenance.

• ❗Please note that this project is still undergoing optimization, and the codes will be updated to support new features and models!

Step1 Download the basic code

git clone --depth 1 https://github.com/zjunlp/PromptKG.git

Step2 Create a virtual environment using Anaconda and enter it.

conda create -n lambdakg python=3.8

conda activate lambdakg

Step3 Enter the task directory and install library

cd PrompKG/lambdaKG

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).

• use text-davince-003 for KGC (Link Prediction)

Before running, please check that you already have the MidRes.json available in dataset, or you may run python ./LLM/create_midres.py to generate it.

And modify the api_key = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" in ./LLM/gpt3kgc.py with your own openai.api_key.

Once it is available, you can proceed to run the code under the LLM folder.

python ./LLM/gpt3kgc.py

• use text-davince-001/002/003 for commonsense reasoning

Before running, please check that you already have downloaded the ATOMIC2020 dataset to dataset/atomic_2020_data and have the file dataset/atomic_2020_data/test.jsonl available in dataset, or you may run python LLM/atomic2020_process.py to generate it.

And modify the api_key = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" in ./LLM/atomic2020_res.py with your own openai.api_key.

Once it is available, you can proceed to run the following code:

python LLM/atomic2020_res.py

After running, result file test_result.json is available under fold dataset/atomic_2020_data.

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

All results are based on the current environment and existing scripts. Your experimental outcomes may exhibit slight deviations from ours.

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
                  Yuqi Zhu and
                  Ningyu Zhang and
                  Jintian Zhang and
                  Siyuan Cheng and
                  Bozhong Tian and
                  Shumin Deng and
                  Feiyu Xiong and
                  Huajun Chen},
  title        = {LambdaKG: {A} Library for Pre-trained Language Model-Based Knowledge
                  Graph Embeddings},
  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    = {Mon, 17 Apr 2023 12:58:49 +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
• KGxBoard