FTMF

FTMF: Few-shot temporal knowledge graph completion based on meta-optimization and Fault-tolerant mechanism This repository contains the implementation of the FTMF architectures described in the paper.

Installation

Install Pytorch (>= 1.1.0)

pip install pytorch

Python 3.x (tested on Python 3.6)

pip install python 3.6

Numpy

pip install numpy

Pandas

pip install pandas

tqdm

pip install tqdm

How to use

run the code:

python train.py 

Dateprocess

To run our code, we need to divide the data set according to the data set partition file first, or divide it according to our own needs. If we want to get the best results, we need to use Complex to pre-train and then embed it into the model.

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}
Gamtching	[link]
MateR	[link]
FSRL	[link]