# UnstructuredModel¶

class UnstructuredModel(*, embedding_dim=50, scoring_fct_norm=1, entity_initializer=<function xavier_normal_>, **kwargs)[source]

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

 hpo_default The default strategy for optimizing the model’s hyper-parameters

Attributes Documentation

hpo_default: ClassVar[Mapping[str, Any]] = {'embedding_dim': {'high': 256, 'low': 16, 'q': 16, 'type': <class 'int'>}, 'scoring_fct_norm': {'high': 2, 'low': 1, 'type': <class 'int'>}}

The default strategy for optimizing the model’s hyper-parameters