ERMLP

class ERMLP(*, embedding_dim=50, hidden_dim=None, entity_initializer=<function uniform_>, relation_initializer=<function uniform_>, **kwargs)

Bases: pykeen.models.base.EntityRelationEmbeddingModel

An implementation of ERMLP from [dong2014].

ERMLP is a multi-layer perceptron based approach that uses a single hidden layer and represents entities and relations as vectors. In the input-layer, for each triple the embeddings of head, relation, and tail are concatenated and passed to the hidden layer. The output-layer consists of a single neuron that computes the plausibility score of the triple:

\[f(h,r,t) = \textbf{w}^{T} g(\textbf{W} [\textbf{h}; \textbf{r}; \textbf{t}]),\]

where \(\textbf{W} \in \mathbb{R}^{k \times 3d}\) represents the weight matrix of the hidden layer, \(\textbf{w} \in \mathbb{R}^{k}\), the weights of the output layer, and \(g\) denotes an activation function such as the hyperbolic tangent.

Initialize the model.

Attributes Summary

hpo_default

The default strategy for optimizing the model's hyper-parameters

Methods Summary

score_h(rt_batch, **kwargs)	Forward pass using left side (head) prediction.
score_hrt(hrt_batch, **kwargs)	Forward pass.
score_t(hr_batch, **kwargs)	Forward pass using right side (tail) prediction.

Attributes Documentation

hpo_default: ClassVar[Mapping[str, Any]] = {'embedding_dim': {'high': 256, 'low': 16, 'q': 16, 'type': <class 'int'>}}

The default strategy for optimizing the model’s hyper-parameters

Methods Documentation

score_h(rt_batch, **kwargs)

Forward pass using left side (head) prediction.

This method calculates the score for all possible heads for each (relation, tail) pair.

Parameters

• rt_batch (LongTensor) – shape: (batch_size, 2), dtype: long The indices of (relation, tail) pairs.

• slice_size – >0 The divisor for the scoring function when using slicing.

• mode – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

Return type

FloatTensor

Returns

shape: (batch_size, num_entities), dtype: float For each r-t pair, the scores for all possible heads.

score_hrt(hrt_batch, **kwargs)

Forward pass.

This method takes head, relation and tail of each triple and calculates the corresponding score.

Parameters

• hrt_batch (LongTensor) – shape: (batch_size, 3), dtype: long The indices of (head, relation, tail) triples.

• mode – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

Return type

FloatTensor

Returns

shape: (batch_size, 1), dtype: float The score for each triple.

score_t(hr_batch, **kwargs)

Forward pass using right side (tail) prediction.

This method calculates the score for all possible tails for each (head, relation) pair.

Parameters

• hr_batch (LongTensor) – shape: (batch_size, 2), dtype: long The indices of (head, relation) pairs.

• slice_size – >0 The divisor for the scoring function when using slicing.

• mode – The pass mode, which is None in the transductive setting and one of “training”, “validation”, or “testing” in the inductive setting.

Return type

FloatTensor

Returns

shape: (batch_size, num_entities), dtype: float For each h-r pair, the scores for all possible tails.