We use five common datasets: WN18, WN18RR, FB15k, FB15k-237, YAGO3-10. Because the total size of datasets is too big to upload. So we upload several examples of datasets in the folder data.
Our code is based on RotatE.
Required environment is shown in /source code/DualQuatE/requirements.txt. 2.2 Content we upload our source codes of DualQuatE, DualQuatE-1 and DualQuatE-2. We implement our algorithms with func- tion DualQuatE in the file: /source code/DualQuatE/codes/model.py, /source code/DualQuatE-1/codes/model.py and /source code/DualQuat-2/codes/model.py respectively.
For example, if you want to test the result of model DualQuatE, you can run the following command in the directory source code/DualQuatE: bash run.sh train DualQuatE YAGO3-10 0 0 512 128 200 24.0 1.0 0.001 100000 4 The log file will be saved in directory source code/DualQuatE/models Note: you need to downlaod the datasets from the above link into the directory source code/DualQuatE/data fist. If you want to test DualQuatE-1 or DualQuatE-2, you need to copy the datasets to the corresponding directory, such as copy source code/DualQuatE/data to the directory source code/DualQuatE-1/.
There are the best config of our models. DualQuatE:
YAGO3-10: bash run.sh train DualQuatE YAGO3-10 3 8 1024 200 300 26.0 1.0 0.0004 100000 1
wn18rr: bash run.sh train DualQuatE wn18rr 0 0 1024 128 100 6.0 0.5 0.0008 80000 8
FB15k-237: bash run.sh train DualQuatE FB15k-237 0 0 1024 256 200 9.0 1.0 0.0001 100000 16
wn18: bash run.sh train DualQuatE wn18 0 0 1024 128 100 12.0 0.5 0.0008 80000 8
FB15k: bash run.sh train DualQuatE FB15k 0 0 1024 32 500 24.0 1.0 0.0003 150000 8
DualQuatE-1:
YAGO3-10: bash run.sh train DualQuatE YAGO3-10 0 0 512 128 200 24.0 1.0 0.0006 100000 4
FB15k-237: bash run.sh train DualQuatE FB15k-237 0 0 1024 32 1000 9.0 1.0 0.00005 100000 8
wn18rr: bash run.sh train DualQuatE wn18rr 0 0 1024 128 200 6.0 0.5 0.0001 80000 8
wn18: bash run.sh train DualQuatE wn18 0 0 512 128 200 14.0 0.5 0.0005 80000 8
FB15k: bash run.sh train DualQuatE FB15k 0 0 1024 32 500 24.0 1.0 0.0003 150000 8
DualQuatE-2: YAGO3-10: bash run.sh train DualQuatE YAGO3-10 0 0 1024 128 200 24.0 1.0 0.0005 100000 4
FB15k-237: bash run.sh train DualQuatE FB15k-237 0 0 1024 128 200 9.0 1.0 0.0001 100000 16 note: with L2 regularization 0.000001 for embeddings 1
wn18rr: bash run.sh train DualQuatE wn18rr 0 0 1024 128 100 6.0 0.5 0.0001 80000 8 note: with L2 regularization 0.000001 for embeddings
wn18: bash run.sh train DualQuatE wn18 0 0 1024 128 100 12.0 0.5 0.0008 80000 8 note: with L2 regularization 0.000001 for embeddings
FB15k: bash run.sh train DualQuatE FB15k 0 0 1024 128 200 24.0 1.0 0.0003 150000 16 note: with L2 regularization 0.000001 for embeddings