ontolearn.lp_generator.helper_classes

Classes

ConceptDescriptionGenerator	Learning problem generator.
KB2Data	This class takes an owl file, loads it into a knowledge base using ontolearn.knowledge_base.KnowledgeBase.

Module Contents

class ontolearn.lp_generator.helper_classes.ConceptDescriptionGenerator(knowledge_base, refinement_operator, depth=2, max_length=10, num_sub_roots=150)

Learning problem generator.

kb

rho

depth = 2

num_sub_roots = 150

max_length = 10

apply_rho(concept)

generate()

class ontolearn.lp_generator.helper_classes.KB2Data(path, storage_path=None, max_num_lps=1000, beyond_alc=False, depth=3, max_child_length=20, refinement_expressivity=0.2, downsample_refinements=True, sample_fillers_count=10, num_sub_roots=50, min_num_pos_examples=1)

This class takes an owl file, loads it into a knowledge base using ontolearn.knowledge_base.KnowledgeBase. A refinement operator is used to generate a large number of concepts, from which we filter and retain the shortest non-redundant concepts. We export each concept and its instances (eventually positive and negative examples) into a json file.

path

max_num_lps = 1000

beyond_alc = False

dl_syntax_renderer

kb

num_examples

min_num_pos_examples = 1

atomic_concept_names

lp_gen

find_optimal_number_of_examples()

generate_descriptions()

sample_examples(pos, neg)

save_data()