Miaquankyz / TPmod

PyTorch implementation of Recurrent Event Network (RE-Net)

Paper: TPmod: A Tendency-Guided Prediction Model for Temporal Knowledge Graph Completion

This repository contains the implementation of the TPmod architectures described in the paper.

Installation

Install PyTorch (>= 1.1.0) following the instuctions on the PyTorch . Our code is written in Python3.

Train and Test

Before running, you should preprocess datasets.

python3 data/DATA_NAME/get_history_new.py

Then, we are ready to train and test. We first train the model.

python3 train.py -dataset DATA_NAME -cuda 0 -dim 256 -lr 1e-4 -epochs 50 -b 1024 -dropout 0.5

We are ready to test!

python3 test.py -dataset DATA_NAME -cuda 0 -dim 256 -lr 1e-4 -epochs 50 -b 1024 -dropout 0.5

Datasets

There are four datasets: two with TGvals: GDELT-5 and ICEWS-250. and two with IGvals: GDELT-5I and ICEWS-250I. Each data folder has 'stat.txt', 'train.txt', 'valid.txt', 'test.txt', 'rel2val.txt' and 'get_history_new.py'.

• 'get_history_new.py': This is for getting history.
• 'rel2val.txt' : This is the file mapping relations to TGvals (IGvals)
• 'stat.txt': First value is the number of entities, and second value is the number of relations.
• 'train.txt', 'valid.txt', 'test.txt': First column is subject entities, second column is relations, and third column is object entities. The fourth column is time.

Baselines

We use the following public codes for baselines and hyperparameters.

Baselines	Code	parameters
TransE	Link	{ lr=0.0001, dim=512,b=512}
TTransE	link	{ lr=0.001, dim=512,b=512}
DE-SimplE	link	{ lr=0.001, dim=128,b=512}
TA-DistMult	link	{ lr=0.001, dim=512,b=1024}
RE-Net	link	{ lr=0.001, dim=256,b=1024}

We implemented RESCAL, DistMult refer to [RotatE](: https://github.com/DeepGraphLearning/ KnowledgeGraphEmbedding.). The user can run the baselines by the following command.

cd ./baselines
bash run.sh train MODEL_NAME DATA_NAME 0 0 512 1024 512 200.0 0.0005 10000 8 0

The user can find implementations in the 'baselines' folder.