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.
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
run the code:
python train.py
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.
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] |