This is the released codes of the following paper:
Kangzheng Liu, Feng Zhao, Guandong Xu, Xianzhi Wang, and Hai Jin. Temporal Knowledge Graph Reasoning via Time-Distributed Representation Learning. ICDM 2022.
python==3.6.5
torch==1.9.0+cu102
dgl-cu102==0.8.0.post1
tqdm==4.62.3
rdflib==5.0.0
numpy==1.19.5
pandas==1.1.5First, extract the repetitive patterns of facts under all historical-timestamp KGs and the global static KG:
python get_history_record.py --dataset YAGOwhere YAGO is the name of one dataset we used in the experiment. Other datasets includes WIKI, ICEWS14, ICEWS18, ICEWS05-15, and GDELT.
Then use the following commands to train the proposed models under different datasets:
cd src
python main.py -d YAGO --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --entity-prediction --gpu 0Finally, use the following commands to evaluate the proposed models under different datasets:
python main.py -d YAGO --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --entity-prediction --gpu 0 --add-static-graph --testWe provide trained models for all datasets. The trained models can be downloaded at https://github.com/Liudaxian1/TrainedModels/tree/main/DHUNET_Models. Then put the trained models in the "./models" folder. Note that the commands in the
YAGO:
python main.py -d YAGO --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --entity-prediction --gpu 0 --add-static-graph --testWIKI:
python main.py -d WIKI --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --entity-prediction --gpu 0 --add-static-graph --testICEWS14:
python main.py -d ICEWS14 --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --entity-prediction --gpu 0 --add-static-graph --testICEWS18:
python main.py -d ICEWS18 --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --entity-prediction --gpu 0 --add-static-graph --testICEWS05-15:
python main.py -d ICEWS05-15 --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --entity-prediction --gpu 0 --add-static-graph --testGDELT:
python main.py -d GDELT --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --entity-prediction --gpu 0 --add-static-graph --testContact us with the following email address: [email protected].
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent