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RoAN: A Relation-oriented Attention Network for Temporal Knowledge Graph Completion

Paper: RoAN: A Relation-oriented Attention Network for Temporal Knowledge Graph Completion

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

Installation

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

How to use?

After installing the requirements, run the following command to reproduce results for RoAN-DES:

$ python main.py -dropout 0.4 -se_prop 0.36 -beta 0.5 -neg_ratio 5 -model RoAN—DES

To reproduce the results for RoAN-DED and RoAN-DET, specify model as RoAN-DED/RoAN-DET as following.

$ python main.py -dropout 0.4 -se_prop 0.36 -beta 0.5 -model RoAN—DED
$ python main.py -dropout 0.4 -se_prop 0.36 -beta 0.5 -model RoAN—DET

Baselines

We use the following public codes for baselines and hyperparameters.