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Graph Based linking prediction

Introduction

The source code for "Context Enhanced Orthogonal Linear Embeddings for Knowledge GraphLink Prediction" accepted by ACL 2020.

Implemented features

Models:

  • RotatE
  • TransE
  • OTE
  • GC-OTE (BiGNNPredOTE)

Evaluation Metrics:

  • MRR, MR, HITS@1, HITS@3, HITS@10 (filtered)

Loss Function:

  • Uniform Negative Sampling
  • Self-Adversarial Negative Sampling

Usage

Knowledge Graph Data:

  • entities.dict: a dictionary map entities to unique ids
  • relations.dict: a dictionary map relations to unique ids
  • train.txt: the KGE model is trained to fit this data set
  • valid.txt: create a blank file if no validation data is available
  • test.txt: the KGE model is evaluated on this data set

Recipes

OTE model on FB15k-237 dataset with GPU 0.

CUDA_VISIBLE_DEVICES=0 python -u scripts/run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path data/FB15k-237 \
 --model OTE \
 -n 256 -b 500 -d 400  \
 -g 15.0 -a 0.5 -adv \
 -lr 0.002  --max_steps 250000 \
 --test_batch_size  16  \ 
 --seed 999 --ote_size 20 \
 --log_steps 1000 --valid_steps 10000  \
 --schedule_steps 25000 \
 --ote_scale 2 \
 -save models/OLE_FB15k-237

GC-OTE with OTE seed model for FB15k-237

CUDA_VISIBLE_DEVICES=0 python -u scripts/run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path data/FB15k-237 \
 --model BiGNNPredOTE \
 -n 256 -b 100 -d 400  \
 -g 12.0 -a 1.4 -adv \
 -lr 0.0002  --max_steps 60000 \
 --test_batch_size  16  \ 
 --seed 999 --ote_size 20 \
 --log_steps 1000 --valid_steps 5000  \
 --schedule_steps 8000 \
 --ote_scale 2 \
 --add_dummy --test_split_num 10 \
 --init_embedding models/OTE_FB15k-237/best \
 -save models/GC_OTE_FB15k-237

OTE model on wn18rr dataset with GPU 0.

CUDA_VISIBLE_DEVICES=0 python -u scripts/run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path data/wn18rr  \
 --model OTE \
 -n 256 -b 500 -d 400  \
 -g 5.0 -a 1.8 -adv \
 -lr 0.001  --max_steps 250000 \
 --test_batch_size  16  \ 
 --seed 999 --ote_size 4  \
 --log_steps 1000 --valid_steps 10000  \
 --schedule_steps 25000 \
 --ote_scale 2 \
 -save models/OTE_wn18rr

GC-OTE with OTE seed model for wn18rr with GPU 0

CUDA_VISIBLE_DEVICES=0 python -u scripts/run.py --do_train \
 --cuda \
 --do_valid \
 --do_test \
 --data_path data/wn18rr  \
 --model BiGNNPredOTE \
 -n 256 -b 100 -d 400  \
 -g 10.0 -a 0.5 -adv \
 -lr 0.00003  --max_steps 60000 \
 --test_batch_size  16  \ 
 --seed 999 --ote_size 4  \
 --log_steps 1000 --valid_steps 5000  \
 --schedule_steps 8000 \
 --ote_scale 2 \
 --add_dummy --test_split_num 10 \  
 --init_embedding models/OLE_wn18rr/best \
 -save models/GC_OTE_wn18rr

Check argparse configuration at scripts/run.py for more arguments and more details.

Test

    CUDA_VISIBLE_DEVICES=$GPU_DEVICE python -u scripts/run.py --do_test --cuda -init $SAVE

Acknowledge

The code is based on RotatE