GraphPlusPyKEENModel

class mowl.models.GraphPlusPyKEENModel(*args, device='cpu', **kwargs)[source]

Bases: KGEModel

This is a wrapper class of pykeen.models.ERModel that allows to use the PyKEEN models in the mOWL framework.

Added in version 0.2.0.

Attributes Summary

`class_embeddings`	Returns a dictionary with class names as keys and class embeddings as values.
`evaluation_model`	Returns the evaluation model.
`individual_embeddings`	Returns a dictionary with individual names as keys and individual embeddings as values.
`object_property_embeddings`	Returns a dictionary with object property names as keys and object property embeddings as values.
`triples_factory`	The triples factory of the model.

Methods Summary

`add_axioms`(*axioms)	This method adds axioms to the dataset contained in the model and reorders the embedding information for each entity accordingly.
`from_pretrained`(model)	This method loads a pretrained model from a file.
`set_kge_method`(kge_method, args, *kwargs)	Set the KGE method of the model.
`train`([epochs])	Triggers the PyKEEN training process.

Attributes Documentation

class_embeddings

evaluation_model

individual_embeddings

object_property_embeddings

triples_factory

The triples factory of the model.

Return type:: pykeen.triples.TriplesFactory

Methods Documentation

add_axioms(*axioms)[source]

This method adds axioms to the dataset contained in the model and reorders the embedding information for each entity accordingly. New entites are initalized with random embedding.

Parameters:: axioms (org.semanticweb.owlapi.model.OWLAxiom) – Axioms to be added to the dataset.

Added in version 0.2.0.

from_pretrained(model)[source]

This method loads a pretrained model from a file.

Parameters:: file_name (str) – Path to the pretrained model file.

Added in version 0.2.0.

set_kge_method(kge_method, *args, **kwargs)[source]

Set the KGE method of the model.

Parameters:: kge_method (pykeen.models.ERModel) – The KGE method.

train(epochs=0)[source]

Triggers the PyKEEN training process.

Parameters:: epochs (int) – Number of epochs to train the model. If None, the value of the epochs parameter passed to the constructor will be used.