GOLD: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection

This is the official code and data repository for the EMNLP2023 Findings paper: Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection.

Environment

Install the project dependencies

pip install -r requirements.txt

Dataset

The ConceptNet and Atomic datasets are already provided in the dataset/ directory. Following previous work[1, 2], we generated 5%, 10%, and 20% noisy triples for both datasets, and these files are stored in errors/ subfolders. Additionally, we applied AMIE[3] on the noisy CSKGs to mine a set of top-ranked rules, which are stored in rules/ subfolders.

Model Training

To train the model, you can use the following command

python gold.py \
--dataset C-05 \
--model_name train \
--epoch 10 \
--batch_size 256 \
--topk 100 \
--ptlm_model sentence-transformers/sentence-t5-xxl \
--lr 0.001 \
--local_lambda 0.1 \
--global_lambda 0.01 \
--neg_cnt 1 \
--seed 5 \
--output_tsv

Scripts for Rule Mining

To mine rules from a knowledge base, please refer to AMIE[3] and follow the instructions provided to generate rules. The resulting files obtained from mining on ConceptNet and Atomic can be found in the scripts/amie-result directory for reference. Then, you can use scripts/process_amie_result.py to process the rules from the CSKG and keep the topk rules for each relation. Here is an example of the code:

python process_amie_result.py --dataset C-05 --topk 500

Reference

[1] "Does william shakespeare REALLY write hamlet? knowledge representation learning with confidence". https://github.com/thunlp/CKRL.git

[2] "Contrastive Knowledge Graph Error Detection". https://github.com/DEEP-PolyU/CAGED_CIKM22.git

[3] "Association Rule Mining under Incomplete Evidence". https://github.com/dig-team/amie.git