Table of Content

• Quick Start

• Simulation

• Ablation settings

• Acknowledgement

• License

Quick Start

Step1. Instanll the required packages by running the following command:

pip install -r requirements.txt

Step2. Specify the gpt-api-key in the config.py file with your openai API key.

Step3. Execute the following command to run llm4ea on D-Y-15k dataset

python infer.py --dataset_name D-Y-15K

Simulation

If you have no access to an OpenAI API, you can run the simulation by running the following command, it syntheises pseudo-labels for the dataset using the true positive rate of 0.5.

python infer.py --dataset_name D-Y-15K --simulate --tpr 0.5

here, the arguement --tpr specifies the true positive rate for the synthesized pseudo-labels.

Ablation settings

There are three optional scripts: infer-baseline.py, infer-active-only.py, and infer-lr-only.py, which are variants of the infer.py script.

• The infer-baseline.py script deactivates both the label refinement and active learning components of the framework, directly training the base EA model, Dual-AMN. This corresponds to the Dual-AMN baseline in the main table.
• The infer-active-only.py script deactivates the label refinement component of the model. This corresponds to the w/o LR ablation setting in the paper.
• The infer-lr-only.py script deactivates the active learning component of the model. This corresponds to the w/o Act ablation setting in the paper.

Acknowledgement

The code is based on PRASE and Dual-AMN, the dataset is from OpenEA benchmark, preprocessed by using the dump file wikidatawiki-20160801-abstract.xml from wikdata. The OpenEA dataset is licensed under the GPLv3 License.

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.