geohyeon/pfoodreq

Code & data accompanying the WSDM 2021 paper "Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph"

PFoodReq

Code & data accompanying the WSDM 2021 paper "Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph".

Architecture

Prerequisites

This code is written in python 3. You will need to install a few python packages in order to run the code. We recommend you to use virtualenv to manage your python packages and environments. Please take the following steps to create a python virtual environment.

• If you have not installed virtualenv, install it with pip install virtualenv.
• Create a virtual environment with virtualenv venv.
• Activate the virtual environment with source venv/bin/activate.
• Install the package requirements with pip install -r requirements.txt.

You will need to download the partial FoodKG used in our experiments from here and move the recipe_kg folder to the data folder in this repo.

Create a KBQA dataset

• Go to data_builder/src folder, run the following cmd:

   python generate_all_qa.py -recipe ../../data/recipe_kg/recipe_kg.json -o ../../data/kbqa_data/ -out_of_domain_ratio 0.1 -split_ratio 0.6 0.2
  

You can also download our generated benchmark from here and move the kbqa_data folder to the data folder in this repo. In the downloaded kbqa_data folder, you will also see additional files used in the KBQA+RecipeSim setting.

Run a KBQA system

Preprocess the data

• Download the pretrained Glove word ebeddings glove.840B.300d.zip and move it to the data folder in this repo.

• Go to the BAMnet/src folder

• Preprocess data

  • Vectorize data

   python build_all_data.py -data_dir ../../data/kbqa_data/ -kb_path ../../data/recipe_kg/recipe_kg.json -out_dir ../data/kbqa
  

  In the message printed out, your will see some data statistics such as vocab_size, num_ent_types, and num_relations. These numbers will be used later when modifying the config file.

  • Fetch the pretrained Glove vectors for our vocabulary.

    python build_pretrained_w2v.py -emb ../../data/glove.840B.300d.txt -data_dir ../data/kbqa/ -out ../data/kbqa/glove_pretrained_300d_w2v.npy
    

• Preprocess data w/o kg augmentation

  • Vectorize data

   python build_all_data.py -data_dir ../../data/kbqa_data/ -kb_path ../../data/recipe_kg/recipe_kg.json -out_dir ../data/kbqa_no_ka  --no_kg_augmentation
  

  • Fetch the pretrained Glove vectors for our vocabulary.

  	 python build_pretrained_w2v.py -emb ../../data/glove.840B.300d.txt -data_dir ../data/kbqa_no_ka/ -out ../data/kbqa_no_ka/glove_pretrained_300d_w2v.npy
  

• Preprocess data w/o query expansion

  • Vectorize data

  python build_all_data.py -data_dir ../../data/kbqa_data/ -kb_path ../../data/recipe_kg/recipe_kg.json -out_dir ../data/kbqa_no_qe --no_query_expansion
  

  • Fetch the pretrained Glove vectors for our vocabulary.

  python build_pretrained_w2v.py -emb ../../data/glove.840B.300d.txt -data_dir ../data/kbqa_no_qe/ -out ../data/kbqa_no_qe/glove_pretrained_300d_w2v.npy
  

• Preprocess data w/o query expansion & kg augmentation

  • Vectorize data

   python build_all_data.py -data_dir ../../data/kbqa_data/ -kb_path ../../data/recipe_kg/recipe_kg.json -out_dir ../data/kbqa_no_qe_ka --no_query_expansion --no_kg_augmentation
  

  • Fetch the pretrained Glove vectors for our vocabulary.

  python build_pretrained_w2v.py -emb ../../data/glove.840B.300d.txt -data_dir ../data/kbqa_no_qe_ka/ -out ../data/kbqa_no_qe_ka/glove_pretrained_300d_w2v.npy
  

Tran/test a KBQA system

• Modify the config file BAMnet/src/config/kbqa.yml to suit your needs. Note that you can start with modifying only the data folder and vocab size (e.g., data_dir, kb_path, pre_word2vec, vocab_size, num_ent_types, and num_relations), and leave other variables as they are.

• Train the KBQA model.

   python train.py -config config/pfoodreq.yml
  

• Test the KBQA model:

   python run_online.py -config config/pfoodreq.yml
  

• Train the KBQA+RecipeSim model.

  python train.py -config config/pfoodreq_similar_recipes.yml
  

• Test the KBQA+RecipeSim model:

  python run_online.py -config config/pfoodreq_similar_recipes.yml
  

Reference

If you found this code useful, please consider citing the following paper:

Yu Chen, Ananya Subburathinam, Ching-Hua Chen and Mohammed J. Zaki. "Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph." In Proceedings of the 14th International Conference on Web Search and Data Mining (WSDM 2021), Mar. 8-12, 2021.

@inproceedings{chen2021personalized,
author    = {Chen, Yu and Subburathinam, Ananya and Chen, Ching-Hua and Zaki, Mohammed J.},
title     = {Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph},
booktitle = {Proceedings of the 14th International Conference on Web Search and Data Mining},
month = {Mar. 8-12,},
year      = {2021}}

Note that we use the BAMnet model as our KBQA system in this application. For more details about the BAMnet model, please refer to the original paper. For more details about the FoodKG, please refer to the original paper.