This is the code for the EMNLP 2022 Findings paper: ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs.
Our methods improves instruction decoding and execution for KGQA via adaptive reasoning, as shown:
We have simple requirements in `requirements.txt'. You can always check if you can run the code immediately.
We use the pre-processed data from: https://drive.google.com/drive/folders/1qRXeuoL-ArQY7pJFnMpNnBu0G-cOz6xv Download it and extract it to a folder named "data".
Acknowledgements:
NSM: Datasets (webqsp, CWQ, MetaQA) / Code.
GraftNet: Datasets (webqsp incomplete, MetaQA) / Code.
To run Webqsp:
python main.py ReaRev --entity_dim 50 --num_epoch 200 --batch_size 8 --eval_every 2 \
--data_folder data/webqsp/ --lm sbert --num_iter 3 --num_ins 2 --num_gnn 2 \
--relation_word_emb True --experiment_name Webqsp322 --name webqsp
To run CWQ:
python main.py ReaRev --entity_dim 50 --num_epoch 100 --batch_size 8 --eval_every 2 \
--data_folder data/CWQ/ --lm sbert --num_iter 2 --num_ins 3 --num_gnn 3 \
--relation_word_emb True --experiment_name CWQ --name cwq
To run MetaQA-3:
python main.py ReaRev --entity_dim 50 --num_epoch 10 --batch_size 8 --eval_every 2 \
--data_folder data/metaqa-3hop/ --lm lstm --num_iter 2 --num_ins 3 --num_gnn 3 \
--relation_word_emb False --experiment_name metaqa3 --name metaqa
For incomplete Webqsp, see 'data/incomplete/' (after obtaining them by GraftNet). If you cannot afford a lot of memory for CWQ, use the '--data_eff' argument (see our arguments in `parsing.py').
We also provide some pretrained ReaRev models (ReaRev_webqsp.ckpt, ReaRev_webqsp_v2.ckpt, ReaRev_CWQ.ckpt). You can download them from here. Please extract them to a folder `checkpoint/pretrain/'.
To reproduce Webqsp results, run:
python main.py ReaRev --entity_dim 50 --num_epoch 200 --batch_size 8 --eval_every 2 --data_folder data/webqsp/ --lm sbert --num_iter 3 --num_ins 2 --num_gnn 3 --relation_word_emb True --load_experiment ReaRev_webqsp.ckpt --is_eval --name webqsp
or
python main.py ReaRev --entity_dim 50 --num_epoch 200 --batch_size 8 --eval_every 2 --data_folder data/webqsp/ --lm sbert --num_iter 3 --num_ins 2 --num_gnn 2 --relation_word_emb True --load_experiment ReaRev_webqsp_v2.ckpt --is_eval --name webqsp
To reproduce CWQ results, run:
python main.py ReaRev --entity_dim 50 --num_epoch 100 --batch_size 8 --eval_every 2 --data_folder .data/CWQ/ --lm sbert --num_iter 2 --num_ins 3 --num_gnn 3 --relation_word_emb True --load_experiment ReaRev_CWQ.ckpt --is_eval --name cwq
| Models | Webqsp | CWQ | MetaQA-3hop |
|---|---|---|---|
| KV-Mem | 46.7 | 21.1 | 48.9 |
| GraftNet | 66.4 | 32.8 | 77.7 |
| PullNet | 68.1 | 45.9 | 91.4 |
| NSM-distill | 74.3 | 48.8 | 98.9 |
| ReaRev | 76.4 | 52.9 | 98.9 |
If you find our code or method useful, please cite our work as
@article{mavromatis2022rearev,
title={ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs},
author={Mavromatis, Costas and Karypis, George},
journal={arXiv preprint arXiv:2210.13650},
year={2022}
}
or
@inproceedings{mavromatis-karypis-2022-rearev,
title = "{R}ea{R}ev: Adaptive Reasoning for Question Answering over Knowledge Graphs",
author = "Mavromatis, Costas and
Karypis, George",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.181",
pages = "2447--2458",
}
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