StructuredEmbedding

class StructuredEmbedding(triples_factory, embedding_dim=50, automatic_memory_optimization=None, scoring_fct_norm=1, loss=None, preferred_device=None, random_seed=None, regularizer=None)

Bases: pykeen.models.base.EntityEmbeddingModel

An implementation of the Structured Embedding (SE) published by [bordes2011].

SE applies role- and relation-specific projection matrices \(\textbf{M}_{r}^{h}, \textbf{M}_{r}^{t} \in \mathbb{R}^{d \times d}\) to the head and tail entities’ embeddings before computing their differences. Then, the \(l_p\) norm is applied and the result is negated such that smaller differences are considered better.

\[f(h, r, t) = - \|\textbf{M}_{r}^{h} \textbf{e}_h - \textbf{M}_{r}^{t} \textbf{e}_t\|_p\]

By employing different projections for the embeddings of the head and tail entities, SE explicitly differentiates the role of an entity as either the subject or object.

Initialize SE.

Parameters

• embedding_dim (int) – The entity embedding dimension \(d\). Is usually \(d \in [50, 300]\).

• scoring_fct_norm (int) – The \(l_p\) norm. Usually 1 for SE.

Attributes Summary

hpo_default

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

Methods Summary

post_parameter_update()	Has to be called after each parameter update.
score_h(rt_batch[, slice_size])	Forward pass using left side (head) prediction.
score_hrt(hrt_batch)	Forward pass.
score_t(hr_batch[, slice_size])	Forward pass using right side (tail) prediction.

Attributes Documentation

hpo_default: ClassVar[Mapping[str, Any]] = {'embedding_dim': {'high': 350, 'low': 50, 'q': 25, 'type': <class 'int'>}, 'scoring_fct_norm': {'high': 2, 'low': 1, 'type': <class 'int'>}}

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

Methods Documentation

post_parameter_update()

Has to be called after each parameter update.

Return type

None

score_h(rt_batch, slice_size=None)

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.

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)

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.

Raises

NotImplementedError – If the method was not implemented for this class.

Return type

FloatTensor

Returns

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

score_t(hr_batch, slice_size=None)

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.

Return type

FloatTensor

Returns

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