This is the code for the EMNLP 2023 Findings paper: [Relation-Aware Question Answering for Heterogeneous Knowledge Graphs](to be continue).
Our methods utilizes information from head-tail entities and the semantic connection between relations to enhance the current relation representation.
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 128 --num_epoch 150 --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 128 --num_epoch 70 --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
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').
| Models | Webqsp | CWQ |
|---|---|---|
| KV-Mem | 46.7 | 21.1 |
| GraftNet | 66.4 | 32.8 |
| PullNet | 68.1 | 45.9 |
| NSM-distill | 74.3 | 48.8 |
| ReaRev | 76.4 | 52.9 |
| RAH-KBQA | 77.2 | 54.4 |
If you find our code or method useful, please cite our work as
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