ontolearn.learning_problem

Learning problem in Ontolearn.

Attributes

logger

Classes

EncodedPosNegLPStandard	Encoded learning problem standard.
PosNegLPStandard	Positive-Negative learning problem standard.
EncodedPosNegUndLP	To be implemented.
PosNegUndLP	To be implemented.

Module Contents

ontolearn.learning_problem.logger

class ontolearn.learning_problem.EncodedPosNegLPStandard(kb_pos, kb_neg, kb_diff, kb_all)

Bases: ontolearn.abstracts.EncodedPosNegLPStandardKind

Encoded learning problem standard.

kb_pos

Positive examples.

Type:

set

kb_neg

Negative examples.

Type:

set

kb_diff

kb_all - (kb_pos + kb_neg).

Type:

set

kb_all

All examples/ all individuals set.

Type:

set

__slots__ = ('kb_pos', 'kb_neg', 'kb_diff', 'kb_all')

kb_pos: set

kb_neg: set

kb_diff: set

kb_all: set

class ontolearn.learning_problem.PosNegLPStandard(pos: Set[owlapy.owl_individual.OWLNamedIndividual], neg: Set[owlapy.owl_individual.OWLNamedIndividual], all_instances: Set[owlapy.owl_individual.OWLNamedIndividual] | None = None)

Bases: ontolearn.abstracts.AbstractLearningProblem

Positive-Negative learning problem standard. .. attribute:: pos

Positive examples.

neg

Negative examples.

all

All examples.

__slots__ = ('pos', 'neg', 'all')

pos

neg

encode_kb(kb: AbstractKnowledgeBase) → EncodedPosNegLPStandard

Provides the encoded learning problem (lp), i.e. the class containing the set of OWLNamedIndividuals as follows:

kb_pos –> the positive examples set, kb_neg –> the negative examples set, kb_all –> all lp individuals / all individuals set, kb_diff –> kb_all - (kb_pos + kb_neg).

Parameters:

kb (PosNegLPStandard) – The knowledge base to encode the learning problem.

Returns:

The encoded learning problem.

Return type:

EncodedPosNegLPStandard

class ontolearn.learning_problem.EncodedPosNegUndLP

Bases: ontolearn.abstracts.EncodedLearningProblem

To be implemented.

class ontolearn.learning_problem.PosNegUndLP(*args, **kwargs)

Bases: ontolearn.abstracts.AbstractLearningProblem

To be implemented.