ELEmbeddings

class mowl.models.ELEmbeddings(dataset, embed_dim=50, margin=0, reg_norm=1, learning_rate=0.001, epochs=1000, batch_size=32768, model_filepath=None, device='cpu')[source]

Bases: EmbeddingELModel

Implementation based on [kulmanov2019].

The idea of this paper is to embed EL by modeling ontology classes as \(n\)-dimensional balls (\(n\)-balls) and ontology object properties as transformations of those \(n\)-balls. For each of the normal forms, there is a distance function defined that will work as loss functions in the optimization framework.

Methods Summary

`eval_method`(data)
`get_embeddings`()
`init_module`()
`load_best_model`()
`train`()	Abstract method for training the model.

Methods Documentation

eval_method(data)[source]

get_embeddings()[source]

init_module()[source]

load_best_model()[source]

train()[source]: Abstract method for training the model. This method must be implemented in children classes