ontolearn.lp_generator.helper_classes

Classes

ConceptDescriptionGenerator	Learning problem generator.
RDFTriples	The knowledge graph/base is converted into triples of the form: individual_i ---role_j---> concept_k or
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

num_sub_roots

max_length

apply_rho(concept)

generate()

class ontolearn.lp_generator.helper_classes.RDFTriples(kb_path, storage_dir=None)

The knowledge graph/base is converted into triples of the form: individual_i —role_j—> concept_k or individual_i —role_j—> individual_k and stored in a txt file for the computation of embeddings.

Graph

kb_path

export_triples(export_folder_name='triples')

class ontolearn.lp_generator.helper_classes.KB2Data(path, storage_dir=None, max_num_lps=1000, 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

dl_syntax_renderer

kb

num_examples

min_num_pos_examples

atomic_concept_names

lp_gen

find_optimal_number_of_examples()

generate_descriptions()

sample_examples(pos, neg)

save_data()