torch_geometric.nn.kge.DistMult

class DistMult(num_nodes: int, num_relations: int, hidden_channels: int, margin: float = 1.0, sparse: bool = False)

Bases: KGEModel

The DistMult model from the “Embedding Entities and Relations for Learning and Inference in Knowledge Bases” paper.

DistMult models relations as diagonal matrices, which simplifies the bi-linear interaction between the head and tail entities to the score function:

\[d(h, r, t) = < \mathbf{e}_h, \mathbf{e}_r, \mathbf{e}_t >\]

Note

For an example of using the DistMult model, see examples/kge_fb15k_237.py.

Parameters:

• num_nodes (int) – The number of nodes/entities in the graph.

• num_relations (int) – The number of relations in the graph.

• hidden_channels (int) – The hidden embedding size.

• margin (float, optional) – The margin of the ranking loss. (default: 1.0)

• sparse (bool, optional) – If set to True, gradients w.r.t. to the embedding matrices will be sparse. (default: False)

reset_parameters()

Resets all learnable parameters of the module.

forward(head_index: Tensor, rel_type: Tensor, tail_index: Tensor) → Tensor

Returns the score for the given triplet.

Parameters:

• head_index (torch.Tensor) – The head indices.

• rel_type (torch.Tensor) – The relation type.

• tail_index (torch.Tensor) – The tail indices.

loss(head_index: Tensor, rel_type: Tensor, tail_index: Tensor) → Tensor

Returns the loss value for the given triplet.

Parameters:

• head_index (torch.Tensor) – The head indices.

• rel_type (torch.Tensor) – The relation type.

• tail_index (torch.Tensor) – The tail indices.