RAH-KBQA [EMNLP 2023]

This is the code for the EMNLP 2023 Findings paper: [Relation-Aware Question Answering for Heterogeneous Knowledge Graphs](to be continue).

Overview

Our methods utilizes information from head-tail entities and the semantic connection between relations to enhance the current relation representation.

Get Started

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.

Training

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').

Results

Cite

If you find our code or method useful, please cite our work as

to be continue

or