Awesome

Glean

This is the source code for paper Dynamic Knowledge Graph based Multi-Event Forecasting appeared in KDD20 (research track)

Songgaojun Deng, Huzefa Rangwala, Yue Ning

Data

We processed some country based datasets from the ICEWS data. Please find an example dataset (partial events are kept.) in this Google Drive Link. The dataset folder (e.g., AFG-raw-part) can be placed in the folder data. A brief introduction of the data file is as follows:

• quadruple.txt includes the structured event information ordered by time.
• text.txt event summary file, where each row corresponds to the event in quadruple.txt
• stat.txt includes the number of entities and event types.
• entity2id.txt entity string to index mapping
• relation2id.txt event type (i.e., relation) string to index mapping
• quadruple_id.txt events represented by the index.

Prerequisites

The code has been successfully tested in the following environment. (For older dgl versions, you may need to modify the code)

• Python 3.7.7
• PyTorch 1.6.0
• dgl 0.5.0
• Sklearn 0.23.2
• Pandas 1.1.1

Example commands executed to build a conda environment (Note: we use Ubuntu with Cuda 9.2)

conda create --name glean python=3.7
conda install pytorch torchvision cudatoolkit=9.2 -c pytorch
pip install dgl-cu92
pip install tqdm
conda install scikit-learn
pip install pandas

Getting Started

Prepare your code

Clone this repo.

git clone https://github.com/amy-deng/glean
cd glean

Prepare your data

Download the dataset from the given link or prepare your own dataset in a similar format. The folder structure is as follows:

- glean
	- data
		- NGA
		- AFG
		- your own dataset
	- src
	- presrc

Preprocessing

Run the files in the presrc folder in the recommended order. Please check the parameters required for each file to run. Here are some brief instructions.

• 0_build_raw_sets.py split the raw data into training, validation, and testing sets.
• 1_get_digraphs.py construct the DGL based event graph data
• 2_get_history.py get historical data for training the actor predictor
• 3_get_token_for_embedding_training.py,4_get_word_embedding.py Some steps to get word embedding from the event summary. Any other method can be applied instead.
• 5_build_word_graphs.pmi.py get word graphs
• 6_get_word_entity_map.py get entity/event type and word mapping
• 7_get_sub_event_dg_from_entity_g.py,8_get_sub_word_g_from_entity_g.py,9_get_scaled_tr_dataset.py construct datasets for training the actor predictor

The processed dataset AFG-example in Google Drive Link can be used directly. Note that only 20 event types are randomly selected for actor prediction in this dataset.

Training and testing

Please run following commands for training and testing. We take the dataset AFG-example as the example.

Event prediction

python train_event_predictor.py --runs 1 --dp ../data/ --gpu 1  -d AFG-example --seq-len 7

Actor prediction

python train_actor_predictor.py --runs 1 --dp ../data/ --gpu 1  -d AFG-example --num-r 20 --seq-len 7

Cite

Please cite our paper if you find this code useful for your research:

@inproceedings{10.1145/3394486.3403209,
author = {Deng, Songgaojun and Rangwala, Huzefa and Ning, Yue},
title = {Dynamic Knowledge Graph Based Multi-Event Forecasting},
year = {2020},
publisher = {Association for Computing Machinery},
booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
pages = {1585–1595},
}