Data and Code for paper Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graph is available for research purposes.

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Deep Learning HGNet
Overview

Data and Code for paper Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graph is available for research purposes.

Results

We apply three KGQA benchmarks to evaluate our approach, ComplexWebQuestions (Talmor and Berant, 2018), LC-QuAD (Trivedi et al., 2017), and WebQSP (Yih et al., 2016).

Dataset Structure Acc. Query Graph Acc. Precision Recall F1-score Hit@1
ComplexWebQuestions 66.96 51.68 65.27 68.44 64.95 65.25
LC-QuAD 78.00 60.90 75.82 75.22 75.10 76.00
WebQSP 79.91 62.63 70.22 74.38 70.61 70.37

Requirements

  • Python == 3.7.0
  • cudatoolkit == 10.1.243
  • cudnn == 7.6.5
  • six == 1.15.0
  • torch == 1.4.0
  • transformers == 4.9.2
  • numpy == 1.19.2
  • SPARQLWrapper == 1.8.5
  • rouge_score == 0.0.4
  • filelock == 3.0.12
  • nltk == 3.6.2
  • absl == 0.0
  • dataclasses == 0.6
  • datasets == 1.9.0
  • jsonlines == 2.0.0
  • python_Levenshtein == 0.12.2
  • Virtuoso SPARQL query service

Data

  • Download and unzip our preprocessed data to ./, you can also running our scripts under ./preprocess to obtain them again.

  • Download our used Freebase and DBpedia. Both of them only contain English triples by removing other languages. Download and install Virtuoso to conduct the SPARQL query service for the downloaded Freebase and DBpedia. Here is a tutorial on how to install Virtuoso and import the knowledge graph into it.

  • Download GloVe Embedding glove.42B.300d.txt and put it to your_glove_path.

  • Download our vocabulary from here. Unzip and put it under ./. It contains our used SPARQL cache for Execution-Guided strategy.

Running Code

1. Training for HGNet

Before training, first set the following hyperparameter in train_cwq.sh, train_lcq.sh, and train_wsp.sh.

--glove_path your_glove_path

Execute the following command for training model on ComplexWebQuestions.

sh train_cwq.sh

Execute the following command for training model on LC-QuAD.

sh train_lcq.sh

Execute the following command for training model on WebQSP.

sh train_wsp.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.

2. Testing for HGNet

Before testing, need to train a model first and set the following hyperparameters in eval_cwq.sh, eval_lcq.sh, and eval_wsp.sh.

--cpt your_trained_model_path
--kb_endpoint your_sparql_service_ip

You can also directly download our trained models from here. Unzip and put it under ./.

Execute the following command for testing the model on ComplexWebQuestions.

sh eval_cwq.sh

Execute the following command for testing the model on LC-QuAD.

sh eval_lcq.sh

Execute the following command for testing the model on WebQSP.

sh eval_wsp.sh
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