ontolearn.learners.alcsat

ALCSAT Learner - SAT-based ALC concept learning.

Classes

ALCSAT

ALCSAT: SAT-based ALC concept learner.

Module Contents

class ontolearn.learners.alcsat.ALCSAT(knowledge_base: AbstractKnowledgeBase, reasoner: owlapy.abstracts.AbstractOWLReasoner | None = None, max_runtime: int | None = 60, max_concept_size: int = 10, start_concept_size: int = 1, operators: Set | None = None, tree_templates: bool = True, type_encoding: bool = True)[source]

Bases: ontolearn.learners.sat_base.SATBaseLearner

ALCSAT: SAT-based ALC concept learner.

This learner uses SAT solvers to find ALC concept expressions that fit positive and negative examples. It encodes the concept learning problem as a SAT problem and uses a Glucose SAT solver to find solutions.

The algorithm incrementally searches for concepts of increasing size (tree depth k) that maximize the accuracy on the given examples.

kb

The knowledge base that the concept learner is using.

Type:: AbstractKnowledgeBase

max_concept_size

Maximum size (depth) of concepts to search for.

Type:: int

start_concept_size

Starting size for incremental search.

Type:: int

operators

Set of ALC operators to use (NEG, AND, OR, EX, ALL).

Type:: Set

tree_templates

Whether to use tree templates for symmetry breaking.

Type:: bool

type_encoding

Whether to use type encoding optimization.

Type:: bool

timeout

Timeout in seconds for the SAT solver (-1 for no timeout).

Type:: float

_best_hypothesis

Best found hypothesis.

Type:: OWLClassExpression

_best_hypothesis_accuracy

Accuracy of the best hypothesis.

Type:: float

_structure

Internal structure representation of the knowledge base.

Type:: Structure

_ind_to_owl

Mapping from internal individual indices to OWL individuals.

Type:: dict

_owl_to_ind

Mapping from OWL individuals to internal indices.

Type:: dict

__slots__ = ('max_concept_size', 'start_concept_size', 'operators', 'tree_templates', 'type_encoding')

name = 'alcsat'

max_concept_size = 10

start_concept_size = 1

operators = None

tree_templates = True

type_encoding = True

fit(lp: PosNegLPStandard)[source]

Find ALC concept expressions that explain positive and negative examples.

Parameters:: lp – Learning problem with positive and negative examples.
Returns:: self