torch_geometric.nn.kge.DistMult

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

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()[source]

Resets all learnable parameters of the module.

forward(head_index: Tensor, rel_type: Tensor, tail_index: Tensor) → Tensor[source]

Returns the score for the given triplet.

Parameters:
loss(head_index: Tensor, rel_type: Tensor, tail_index: Tensor) → Tensor[source]

Returns the loss value for the given triplet.

Parameters: