# SimplE¶

class SimplE(triples_factory, embedding_dim=200, automatic_memory_optimization=None, loss=None, preferred_device=None, random_seed=None, regularizer=None, clamp_score=None)[source]

An implementation of SimplE [kazemi2018].

SimplE is an extension of canonical polyadic (CP), an early tensor factorization approach in which each entity $$e \in \mathcal{E}$$ is represented by two vectors $$\textbf{h}_e, \textbf{t}_e \in \mathbb{R}^d$$ and each relation by a single vector $$\textbf{r}_r \in \mathbb{R}^d$$. Depending whether an entity participates in a triple as the head or tail entity, either $$\textbf{h}$$ or $$\textbf{t}$$ is used. Both entity representations are learned independently, i.e. observing a triple $$(h,r,t)$$, the method only updates $$\textbf{h}_h$$ and $$\textbf{t}_t$$. In contrast to CP, SimplE introduces for each relation $$\textbf{r}_r$$ the inverse relation $$\textbf{r'}_r$$, and formulates its the interaction model based on both:

$f(h,r,t) = \frac{1}{2}\left(\left\langle\textbf{h}_h, \textbf{r}_r, \textbf{t}_t\right\rangle + \left\langle\textbf{h}_t, \textbf{r'}_r, \textbf{t}_h\right\rangle\right)$

Therefore, for each triple $$(h,r,t) \in \mathbb{K}$$, both $$\textbf{h}_h$$ and $$\textbf{h}_t$$ as well as $$\textbf{t}_h$$ and $$\textbf{t}_t$$ are updated.

Initialize the entity embedding model.

Parameters

relation_dim – The relation embedding dimensionality. If not given, defaults to same size as entity embedding dimension.

Constructor of the base class pykeen.models.Model

Constructor of the base class pykeen.models.EntityEmbeddingModel

Attributes Summary

 hpo_default The default strategy for optimizing the model’s hyper-parameters loss_default_kwargs The default parameters for the default loss function class regularizer_default_kwargs The power sum settings used by [trouillon2016] for SimplE

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': 350, 'low': 50, 'q': 25, 'type': <class 'int'>}}

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

loss_default_kwargs: ClassVar[Optional[Mapping[str, Any]]] = {}

The default parameters for the default loss function class

regularizer_default_kwargs: ClassVar[Optional[Mapping[str, Any]]] = {'normalize': True, 'p': 2.0, 'weight': 20}

The power sum settings used by [trouillon2016] for SimplE

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.