ontolearn.lp_generator

Submodules

Classes

`LPGen`
`KB2Data`	This class takes an owl file, loads it into a knowledge base using ontolearn.knowledge_base.KnowledgeBase.

Package Contents

class ontolearn.lp_generator.LPGen(kb_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)[source]

lp_gen

generate()[source]

class ontolearn.lp_generator.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)[source]

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()[source]

generate_descriptions()[source]

sample_examples(pos, neg)[source]

save_data()[source]