AutoKG

Code and Data for the paper "LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities"

🌄Overview

The overview of our work. There are three main components: 1) Basic Evaluation: detailing our assessment of large models (text-davinci-003, ChatGPT, and GPT-4), in both zero-shot and one-shot settings, using performance data from fully supervised state-of-the-art models as benchmarks; 2) Virtual Knowledge Extraction: an examination of large models' virtual knowledge capabilities on the constructed VINE dataset; and 3) Automatic KG: the proposal of utilizing multiple agents to facilitate the construction and reasoning of KGs.

🌟 Evaluation

Data Preprocess

The datasets that we used in our experiments are as follows:

KG Construction
- DuIE2.0
- SciERC
- RE-TACRED
- MAVEN
You can download the dataset from the above address, and you can also find the data used in this experiment directly from the corresponding "datas" folder like DuIE2.0.
KG Reasoning
- FB15k-237
- ATOMIC2020
Question Answering
- FreebaseQA
- MetaQA

The expected structure of files is:

AutoKG
 |-- KG Construction
 |    |-- DuIE2.0
 |    |    |-- datas                    #dataset
 |    |    |-- prompts                  #0-shot/1-shot prompts
 |    |    |-- duie_processor.py        #preprocess data
 |    |    |-- duie_prompts.py          #generate prompts
 |	  |--MAVEN
 |    |    |-- datas                    #dataset
 |    |    |-- prompts                  #0-shot/1-shot prompts
 |    |    |-- maven_processor.py       #preprocess data
 |    |    |-- maven_prompts.py         #generate prompts
 |    |--RE-TACRED
 |    |    |-- datas                    #dataset
 |    |    |-- prompts                  #0-shot/1-shot prompts
 |    |    |-- retacred_processor.py    #preprocess data
 |    |    |-- retacred_prompts.py      #generate prompts
 |    |--SciERC
 |    |    |-- datas                    #dataset
 |    |    |-- prompts                  #0-shot/1-shot prompts
 |    |    |-- scierc_processor.py      #preprocess data
 |    |    |-- scierc_prompts.py        #generate prompts
 |-- KG Reasoning (Link Prediction)
 |    |-- FB15k-237
 |    |    |-- data                     #sample data
 |    |    |-- prompts                  #0-shot/1-shot prompts
 |    |-- ATOMIC2020
 |    |    |-- data                     #sample data
 |    |    |-- prompts                  #0-shot/1-shot prompts
 |    |    |-- system_eval              #eval for ATOMIC2020

How to Run

KG Construction(Use DuIE2.0 as an example)
```
cd KG Construction
python duie_processor.py 
python duie_prompts.py
```
Then we’ll get 0-shot/1-shot prompts in the folder “prompts”
KG Reasoning
Question Answering

🕵️Virtual Knowledge Extraction

The VINE dataset we built is available here.

Do the following code to generate prompts:

cd Virtual Knowledge Extraction
python VINE_processor.py
python VINE_prompts.py

🤖AutoKG

Our AutoKG code is based on CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society and a LangChain implementation of the paper, you can get more details through this link.

Change the OPENAI_API_KEY in Autokg.py
Change the SERPAPI_API_KEY in RE_CAMEL.py .( You can get more information in serpapi )

Run the Autokg.py script.

cd AutoKG
python Autokg.py

Citation

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

@article{zhu2023llms,
  title={LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities},
  author={Zhu, Yuqi and Wang, Xiaohan and Chen, Jing and Qiao, Shuofei and Ou, Yixin and Yao, Yunzhi and Deng, Shumin and Chen, Huajun and Zhang, Ningyu},
  journal={arXiv preprint arXiv:2305.13168},
  year={2023}
}

zjunlp/AutoKG