# TransE¶

class TransE(triples_factory, embedding_dim=50, scoring_fct_norm=1, loss=None, preferred_device=None, random_seed=None, regularizer=None)[source]

TransE models relations as a translation from head to tail entities in $$\textbf{e}$$ [bordes2013].

$\textbf{e}_h + \textbf{e}_r \approx \textbf{e}_t$

This equation is rearranged and the $$l_p$$ norm is applied to create the TransE interaction function.

$f(h, r, t) = - \|\textbf{e}_h + \textbf{e}_r - \textbf{e}_t\|_{p}$

While this formulation is computationally efficient, it inherently cannot model one-to-many, many-to-one, and many-to-many relationships. For triples $$(h,r,t_1), (h,r,t_2) \in \mathcal{K}$$ where $$t_1 \neq t_2$$, the model adapts the embeddings in order to ensure $$\textbf{e}_h + \textbf{e}_r \approx \textbf{e}_{t_1}$$ and $$\textbf{e}_h + \textbf{e}_r \approx \textbf{e}_{t_2}$$ which results in $$\textbf{e}_{t_1} \approx \textbf{e}_{t_2}$$.

Initialize TransE.

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

• scoring_fct_norm (int) – The $$l_p$$ norm applied in the interaction function. Is usually 1 or 2..

Attributes Summary

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

Methods Summary

 score_h(rt_batch) Forward pass using left side (head) prediction. score_hrt(hrt_batch) Forward pass. score_t(hr_batch) 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'>}, 'scoring_fct_norm': {'high': 2, 'low': 1, 'type': <class 'int'>}}

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

Methods Documentation

score_h(rt_batch)[source]

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)[source]

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)[source]

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.