UnstructuredModel¶
-
class
UnstructuredModel
(triples_factory, embedding_dim=50, scoring_fct_norm=1, loss=None, predict_with_sigmoid=False, preferred_device=None, random_seed=None, entity_initializer=<function xavier_normal_>)[source]¶ Bases:
Generic
[pykeen.typing.HeadRepresentation
,pykeen.typing.RelationRepresentation
,pykeen.typing.TailRepresentation
],pykeen.models.nbase._NewAbstractModel
An implementation of the Unstructured Model (UM) published by [bordes2014].
UM computes the distance between head and tail entities then applies the \(l_p\) norm.
\[f(h, r, t) = - \|\textbf{e}_h - \textbf{e}_t\|_p^2\]A small distance between the embeddings for the head and tail entity indicates a plausible triple. It is appropriate for networks with a single relationship type that is undirected.
Warning
In UM, neither the relations nor the directionality are considered, so it can’t distinguish between them. However, it may serve as a baseline for comparison against relation-aware models.
Initialize UM.
- Parameters
Attributes Summary
The default strategy for optimizing the model’s hyper-parameters
Attributes Documentation