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Performance

Predictive Performance

We report the filtered MR, MRR, Hits@1,3,10 for the most common datasets used in literature.

FB15K-237

Model MR MRR Hits@1 Hits@3 Hits@10 Hyperparameters
TransE 153 0.31 0.22 0.35 0.51 k: 1000; epochs: 4000; eta: 50; loss: self_adversarial; loss_params: alpha: 0.5; margin: 5; optimizer: adam; optimizer_params: lr: 0.0001; embedding_model_params: norm: 1; normalize_ent_emb: false; seed: 0; batches_count: 60
DistMult 568 0.29 0.20 0.32 0.47 k: 400; epochs: 4000; eta: 50; loss: self_adversarial; loss_params: alpha: 1; margin: 1; regularizer: LP; regularizer_params: lambda: 0.0001; p: 2; optimizer: adam; optimizer_params: lr: 0.0005; seed: 0; normalize_ent_emb: false; batches_count: 50
ComplEx 519 0.30 0.20 0.33 0.48 k: 350; epochs: 4000; eta: 30; loss: self_adversarial; loss_params: alpha: 1; margin: 0.5; optimizer: adam; optimizer_params: lr: 0.0001; seed: 0; batches_count: 50
HolE 297 0.28 0.19 0.31 0.46 k: 350; epochs: 4000; eta: 30; loss: self_adversarial; loss_params: alpha: 1 margin: 0.5; optimizer: adam; optimizer_params: lr: 0.0001; seed: 0; batches_count: 50

Note

FB15K-237 validation and test sets include triples with entities that do not occur in the training set. We found 8 unseen entities in the validation set and 29 in the test set. In the experiments we excluded the triples where such entities appear (9 triples in from the validation set and 28 from the test set).

WN18RR

Model MR MRR Hits@1 Hits@3 Hits@10 Hyperparameters
TransE 1536 0.23 0.07 0.35 0.51 k: 200; epochs: 4000; eta: 20; loss: self_adversarial; loss_params: margin: 1; optimizer: adam; optimizer_params: lr: 0.0001; regularizer: LP; regularizer_params: lambda: 1.0e-05; p: 1; seed: 0; normalize_ent_emb: false; embedding_model_params: norm: 1; batches_count: 100;
DistMult 6853 0.44 0.42 0.45 0.50 k: 200; epochs: 4000; eta: 20; loss: self_adversarial; loss_params: margin: 1; optimizer: adam; optimizer_params: lr: 0.0005; seed: 0; normalize_ent_emb: false; batches_count: 25
ComplEx 8214 0.44 0.41 0.45 0.50 k: 200; epochs: 4000; eta: 20; loss: nll; loss_params: margin: 1; optimizer: adam; optimizer_params: lr: 0.0005; seed: 0; batches_count: 10;
HolE 7305 0.47 0.43 0.48 0.53 k: 200; epochs: 4000; eta: 20; loss: self_adversarial; loss_params: margin: 1; optimizer: adam; optimizer_params: lr: 0.0005; seed: 0; batches_count: 50

Note

WN18RR validation and test sets include triples with entities that do not occur in the training set. We found 198 unseen entities in the validation set and 209 in the test set. In the experiments we excluded the triples where such entities appear (210 triples in from the validation set and 210 from the test set).

YAGO3-10

Model MR MRR Hits@1 Hits@3 Hits@10 Hyperparameters
TransE 574 0.24 0.15 0.26 0.41 k: 1000; epochs: 4000; eta: 50; loss: self_adversarial; loss_params: alpha: 0.5; margin: 5; optimizer: adam; optimizer_params: lr: 0.0001; embedding_model_params: norm: 1; normalize_ent_emb: false; seed: 0; batches_count: 150
DistMult 4903 0.49 0.41 0.54 0.63 k: 400; epochs: 4000; eta: 50; loss: self_adversarial; loss_params: alpha: 1; margin: 1; optimizer: adam; optimizer_params: lr: 0.0005; regularizer: LP; regularizer_params: lambda: 0.0001; p: 2; seed: 0; normalize_ent_emb: false; batches_count: 100
ComplEx 7266 0.50 0.42 0.55 0.65 k: 350; epochs: 4000; eta: 30; loss: self_adversarial; loss_params: alpha: 1; margin: 0.5; optimizer: adam; optimizer_params: lr: 0.0001; seed: 0; batches_count: 100
HolE 6201 0.50 0.41 0.55 0.65 k: 350; epochs: 4000; eta: 30; loss: self_adversarial; loss_params: alpha: 1; margin: 0.5; optimizer: adam; optimizer_params: lr: 0.0001; seed: 0; batches_count: 100

Note

YAGO3-10 validation and test sets include triples with entities that do not occur in the training set. We found 22 unseen entities in the validation set and 18 in the test set. In the experiments we excluded the triples where such entities appear (22 triples in from the validation set and 18 from the test set).

FB15K

Warning

The dataset includes a large number of inverse relations, and its use in experiments has been deprecated. Use FB15k-237 instead.

Model MR MRR Hits@1 Hits@3 Hits@10 Hyperparameters
TransE 105 0.55 0.39 0.68 0.79 k: 150; epochs: 4000; eta: 5; loss: pairwise; loss_params: margin: 0.5; optimizer: adam; optimizer_params: lr: 0.0001; regularizer: LP; regularizer_params: lambda: 0.0001; p: 2; seed: 0; embedding_model_params: norm: 1; normalize_ent_emb: false; batches_count: 10
DistMult 177 0.79 0.74 0.82 0.86 k: 200; epochs: 4000; eta: 20; loss: self_adversarial; loss_params: margin: 1; optimizer: adam; optimizer_params: lr: 0.0005; seed: 0; normalize_ent_emb: false; batches_count: 50
ComplEx 188 0.79 0.76 0.82 0.86 k: 200; epochs: 4000; eta: 20; loss: self_adversarial; loss_params: margin: 1; optimizer: adam; optimizer_params: lr: 0.0005; seed: 0; batches_count: 100
HolE 212 0.80 0.76 0.83 0.87 k: 200; epochs: 4000; eta: 20; loss: self_adversarial; loss_params: margin: 1; optimizer: adam; optimizer_params: lr: 0.0005; seed: 0; batches_count: 50

WN18

Warning

The dataset includes a large number of inverse relations, and its use in experiments has been deprecated. Use WN18RR instead.

Model MR MRR Hits@1 Hits@3 Hits@10 Hyperparameters
TransE 446 0.50 0.18 0.81 0.89 k: 150; epochs: 4000; eta: 5; loss: pairwise; loss_params: margin: 0.5; optimizer: adam; optimizer_params: lr: 0.0001; regularizer: LP; regularizer_params: lambda: 0.0001; p: 2; embedding_model_params: norm: 1; normalize_ent_emb: false; seed: 0; batches_count: 10
DistMult 746 0.83 0.73 0.92 0.95 k: 200; epochs: 4000; eta: 20; loss: nll; loss_params: margin: 1; optimizer: adam; optimizer_params: lr: 0.0005; seed: 0; normalize_ent_emb: false; batches_count: 50
ComplEx 715 0.94 0.94 0.95 0.95 k: 200; epochs: 4000; eta: 20; loss: nll; loss_params: margin: 1; optimizer: adam; optimizer_params: lr: 0.0005; seed: 0; batches_count: 50
HolE 658 0.94 0.93 0.94 0.95 k: 200; epochs: 4000; eta: 20; loss: self_adversarial; loss_params: margin: 1; optimizer: adam; optimizer_params: lr: 0.0005; seed: 0; batches_count: 50

To reproduce the above results:

$ cd experiments
$ python predictive_performance.py

Note

Running predictive_performance.py on all datasets, for all models takes ~43 hours on an Intel Xeon Gold 6142, 64 GB Ubuntu 16.04 box equipped with a Tesla V100 16GB.

Experiments can be limited to specific models-dataset combinations as follows:

$ python predictive_performance.py -h
usage: predictive_performance.py [-h] [-d {fb15k,fb15k-237,wn18,wn18rr,yago310}]
                                 [-m {complex,transe,distmult,hole}]

optional arguments:
  -h, --help            show this help message and exit
  -d {fb15k,fb15k-237,wn18,wn18rr,yago310}, --dataset {fb15k,fb15k-237,wn18,wn18rr,yago310}
  -m {complex,transe,distmult,hole}, --model {complex,transe,distmult,hole}

Runtime Performance

Training the models on FB15K-237 (k=200, eta=2, batches_count=100, loss=nll), on an Intel Xeon Gold 6142, 64 GB Ubuntu 16.04 box equipped with a Tesla V100 16GB gives the following runtime report:

model seconds/epoch
ComplEx 3.19
TransE 3.26
DistMult 2.61
HolE 3.21
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