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[WWWJ 2024] LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
| Date | Stars |
|---|---|
| 2026-07-31 | 470 |
| 2026-08-02 | 472 |
| 2026-08-06 | 472 |
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# AutoKG [](https://github.com/zjunlp/AutoKG) [](https://opensource.org/licenses/MIT)  Code and Data for the paper "[LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities](Pdf/LLMs_for_Knowledge_Graph_Construction_and_Reasoning__Recent_Capabilities_and_Future_Opportunities1.pdf)" ## 🌄Overview <div align=center><img src="figs/overall_f.jpg" alt="Overview" width="700px" /></div> 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](https://www.luge.ai/#/luge/dataDetail?id=5) - [SciERC](http://nlp.cs.washington.edu/sciIE/) - [RE-TACRED](https://github.com/gstoica27/Re-TACRED) - [MAVEN](https://github.com/THU-KEG/MAVEN-dataset/tree/main) 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](https://github.com/zjunlp/AutoKG/tree/4edd8ad698a1ad3b90abb34d79f299d0e839a28c/KG%20Construction/DuIE2.0/datas). - KG Reasoning - [FB15k-237](https://github.com/zjunlp/Relphormer/tree/main/dataset/fb15k-237) - [ATOMIC2020](https://allenai.org/data/atomic-2020) - 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) ```bash 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](https://github.com/zjunlp/AutoKG/tree/94d1a9450cc55fc162366f5ba7d7a6d5b54c0236/Virtual%20Knowledge%20Extraction).** Do the following code to generate prompts: ```bash cd Virtual Knowledge Extraction python VINE_
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matched fp:8d8f9fbc70fa6433, topic:knowledge-graph, desc:knowledge graph
matched fp:8d8f9fbc70fa6433, topic:large-language-models
matched fp:8d8f9fbc70fa6433, topic:information-extraction
matched fp:8d8f9fbc70fa6433, topic:chatgpt