# ProjE

class ProjE(*, embedding_dim=50, inner_non_linearity=None, entity_initializer=<function xavier_uniform_>, relation_initializer=<function xavier_uniform_>, **kwargs)[source]

Bases: ERModel

An implementation of ProjE from [shi2017].

ProjE is a neural network-based approach with a combination and a projection layer. The interaction model first combines $$h$$ and $$r$$ by following combination operator:

$\textbf{h} \otimes \textbf{r} = \textbf{D}_e \textbf{h} + \textbf{D}_r \textbf{r} + \textbf{b}_c$

where $$\textbf{D}_e, \textbf{D}_r \in \mathbb{R}^{k \times k}$$ are diagonal matrices which are used as shared parameters among all entities and relations, and $$\textbf{b}_c \in \mathbb{R}^{k}$$ represents the candidate bias vector shared across all entities. Next, the score for the triple $$(h,r,t) \in \mathbb{K}$$ is computed:

$f(h, r, t) = g(\textbf{t} \ z(\textbf{h} \otimes \textbf{r}) + \textbf{b}_p)$

where $$g$$ and $$z$$ are activation functions, and $$\textbf{b}_p$$ represents the shared projection bias vector.

Initialize the model.

Parameters

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

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

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

The default parameters for the default loss function class