jboru / aqgnet

Data and Code for IJCAI 2020 paper Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base is available for research purposes.

Requirements

• Python 3.6
• Pytorch 1.2.0
• DBpedia Version 2016-04
• SPARQL service (constructed by Virtuoso or Apache Jena Fuseki)

Running Code

Download Glove Embedding and put glove.42B.300d.txt under ./data/ directory.

1. Preprocess data for AQGNet

cd ./preprocess
sh run_me.sh

2. Training for AQGNet

Modify the following content in ./train.sh.

devices=$1

• Replace $1 with the id of the GPU to be used, such as 0.
  Then, execute the following command for training.

sh train.sh

The trained model file is saved under ./runs directory.
The path format of the trained model is ./runs/RUN_ID/checkpoints/best_snapshot_epoch_xx_best_val_acc_xx_model.pt

3. Testing for AQGNet

Modify the following content in ./eval.sh.

devices=$1
save_name=$2
dbpedia_endpoint=$3

• Replace $1 with the id of the GPU to be used.
• Replace $2 with the path of the trained model.
• Replace $3 with the address of the established DBpedia SPARQL service, such as http://10.201.158.104:3030/dbpedia/sparql

The result of AQGNet structure prediction is saved under the used model directory. The path format of result is ./runs/RUN_ID/results.pkl.
Then, execute the following command for structure prediction.

sh eval.sh

4. Generate candidate queries

Modify the following content in ./generate_queries.sh.

test_data=$1            # structure prediction results path
dbpedia_endpoint=$2     # http://10.201.158.104:3030/dbpedia/sparql

The candidate queries for the training set, valid set, and test set are saved under ./data directory.

5. Preprocess data for query ranking model

cd ./query_ranking
sh run_me.sh

6. Training for query ranking model

Modify the following content in ./query_ranking/train.sh.

devices=$1

• Replace $1 with the id of the GPU to be used. Then, execute the following command for training query ranking model.

cd ./query_ranking
sh train.sh

The trained query ranking model file is saved under ./query_ranking/runs directory.

6. Test for query ranking model

Modify the following content in ./query_ranking/eval.sh.

devices=$1
save_name=$2
dbpedia_endpoint=$3

• Replace $1 with the id of the GPU to be used.
• Replace $2 with the path of the trained model.
• Replace $3 with the address of the established DBpedia SPARQL service, such as http://10.201.158.104:3030/dbpedia/sparql.

Then, execute the following command for the final results of question answering.

cd ./query_ranking
sh eval.sh

Citation

If you use AQGNet, please cite the following work.

@inproceedings{DBLP:conf/ijcai/ChenLHQ20,
  author    = {Yongrui Chen and
               Huiying Li and
               Yuncheng Hua and
               Guilin Qi},
  editor    = {Christian Bessiere},
  title     = {Formal Query Building with Query Structure Prediction for Complex
               Question Answering over Knowledge Base},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI} 2020 [scheduled for July 2020, Yokohama,
               Japan, postponed due to the Corona pandemic]},
  pages     = {3751--3758},
  publisher = {ijcai.org},
  year      = {2020},
  url       = {https://doi.org/10.24963/ijcai.2020/519},
  doi       = {10.24963/ijcai.2020/519},
  timestamp = {Mon, 13 Jul 2020 18:09:15 +0200},
  biburl    = {https://dblp.org/rec/conf/ijcai/ChenLHQ20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}