FolkScope

Sourcecode and datasets for the paper "FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery" ([arXiv] [Amazon Science])

Datasets

We release product metadata, the annotated training datasets and the whole poplulated generations with both plausibility and typicality scores, and recommendation data in the shared folders.

Implementation

Package Dependencies

• nltk
• wandb
• pandas
• sklearn
• evalaute
• datasets
• tqdm
• sentencepiece
• accelerate==0.9.0
• torch==1.10.1+cu111
• transformers==4.20.0
• python-igraph == 0.9.11
• stanfordnlp==0.2.0

1. Prompting Generation

bash scripts/run_generation.sh

2. Classifier Training and Inference

bash scripts/run_training.sh
bash scripts/run_inference.sh

3. Knowledge Graph Construction

Kind reminder: please ensure that you have more than 100GB memory for pattern mining. Otherwise, please set a smaller num_workers

bash scripts/run_mining.sh
bash scripts/run_match.sh
bash scripts/run_conceptualization.sh

Citation

Please kindly cite the following paper if you found our method and resources helpful!

@inproceedings{yu-etal-2023-folkscope,
    title = "{F}olk{S}cope: Intention Knowledge Graph Construction for {E}-commerce Commonsense Discovery",
    author = "Yu, Changlong  and
      Wang, Weiqi  and
      Liu, Xin  and
      Bai, Jiaxin  and
      Song, Yangqiu  and
      Li, Zheng  and
      Gao, Yifan  and
      Cao, Tianyu  and
      Yin, Bing",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.76",
    pages = "1173--1191",
}