This is the source code for: Context-aware Entity Typing in Knowledge Graphs.

Related tags

Deep Learning CET
Overview

Context-aware Entity Typing in Knowledge Graphs

This is the source code for: Context-aware Entity Typing in Knowledge Graphs.

Requirements

  • Python 3
  • PyTorch >= 1.6.0
  • dgl >= 0.5.3

Usage

Data preprocessing:

cd data
python preprocess.py --dataset FB15kET
python preprocess.py --dataset YAGO43kET

Train:

########### FB15kET ###########
# CET
python run.py --model CET --dataset FB15kET --load_ET --load_KG --neighbor_sampling \
--hidden_dim 100 --temperature 0.5 --lr 0.001 --loss FNA --beta 4.0 --cuda

# R-GCN
python run.py --model RGCN --dataset FB15kET --load_ET --load_KG --neighbor_sampling \
--hidden_dim 100 --lr 0.001 --loss FNA --beta 3.0 --cuda

# CompGCN
python run.py --model CompGCN --dataset FB15kET --load_ET --load_KG --neighbor_sampling \
--hidden_dim 100 --lr 0.001 --loss FNA --activation relu --cuda

########### YAGO43kET ###########
# CET
python run.py --model CET --dataset YAGO43kET --load_ET --load_KG --neighbor_sampling \
--hidden_dim 100 --temperature 0.5 --lr 0.001 --loss FNA --beta 2.0 --cuda

# R-GCN
python run.py --model RGCN --dataset YAGO43kET --load_ET --load_KG --neighbor_sampling \
--hidden_dim 100 --lr 0.001 --loss FNA --beta 2.0 --cuda

# CompGCN
python run.py --model CompGCN --dataset YAGO43kET --load_ET --load_KG --neighbor_sampling \
--hidden_dim 100 --lr 0.001 --loss FNA --activation relu --cuda

The KGE baselines can be found in KGE_baselines.

Acknowledgement

We refer to the code of DGL. Thanks for their contributions.

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Comments
  • Train KGE baselines with FNA loss

    Train KGE baselines with FNA loss

    Hi,

    Thank you for the wonderful and inspiring work. I am wondering if have you tried to train KGE baselines using FNA loss and compare the performance with your proposed model. If yes, could you kindly provide the script for such an experiment? Many thanks.

    opened by yunchengwang 0
Owner
null
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