# TransEο

class dgl.nn.pytorch.link.TransE(num_rels, feats, p=1)[source]ο

Bases: Module

Similarity measure from Translating Embeddings for Modeling Multi-relational Data

Mathematically, it is defined as follows:

$- {\| h + r - t \|}_p$

where $$h$$ is the head embedding, $$r$$ is the relation embedding, and $$t$$ is the tail embedding.

Parameters:
• num_rels (int) β Number of relation types.

• feats (int) β Embedding size.

• p (int, optional) β The p to use for Lp norm, which can be 1 or 2.

rel_embο

The learnable relation type embedding.

Type:

torch.nn.Embedding

Examples

>>> import dgl
>>> import torch as th
>>> from dgl.nn import TransE

>>> # input features
>>> num_nodes = 10
>>> num_edges = 30
>>> num_rels = 3
>>> feats = 4

>>> scorer = TransE(num_rels=num_rels, feats=feats)
>>> g = dgl.rand_graph(num_nodes=num_nodes, num_edges=num_edges)
>>> src, dst = g.edges()
>>> h = th.randn(num_nodes, feats)
>>> h_head = h[src]
>>> h_tail = h[dst]
>>> # Randomly initialize edge relation types for demonstration
>>> rels = th.randint(low=0, high=num_rels, size=(num_edges,))
>>> scorer(h_head, h_tail, rels).shape
torch.Size([30])

forward(h_head, h_tail, rels)[source]ο

## Descriptionο

Score triples.

param h_head:

Head entity features. The tensor is of shape $$(E, D)$$, where $$E$$ is the number of triples, and $$D$$ is the feature size.

type h_head:

torch.Tensor

param h_tail:

Tail entity features. The tensor is of shape $$(E, D)$$, where $$E$$ is the number of triples, and $$D$$ is the feature size.

type h_tail:

torch.Tensor

param rels:

Relation types. It is a LongTensor of shape $$(E)$$, where $$E$$ is the number of triples.

type rels:

torch.Tensor

returns:

The triple scores. The tensor is of shape $$(E)$$.

rtype:

torch.Tensor

reset_parameters()[source]ο

## Descriptionο

Reinitialize learnable parameters.