ontolearn.semantic_caching

python examples/retrieval_eval.py

Classes

CacheWithEviction

Functions

concept_generator(path_kg)
get_shuffled_concepts(path_kg, data_name)	Shuffle the generated concept and save it in a folder for reproducibility
concept_retrieval(→ Set[str])
semantic_caching_size(func, cache_size, ...)	This function implements the semantic caching algorithm for ALC concepts as presented in the paper
non_semantic_caching_size(func, cache_size)	This function implements a caching algorithm for ALC concepts without semantics.
retrieve(→ Tuple[Set[str], Set[str]])	Retrieve instances with neural reasoner
retrieve_other_reasoner(expression, path_kg[, ...])	Retrieve instances with symbolic reasoners
run_semantic_cache(path_kg, path_kge, cache_size, ...)	Return cache performnace with semantics
run_non_semantic_cache(path_kg, path_kge, cache_size, ...)	Return cache performnace without any semantics

Module Contents

ontolearn.semantic_caching.concept_generator(path_kg)

ontolearn.semantic_caching.get_shuffled_concepts(path_kg, data_name)

Shuffle the generated concept and save it in a folder for reproducibility

ontolearn.semantic_caching.concept_retrieval(retriever_func, c) → Set[str]

class ontolearn.semantic_caching.CacheWithEviction(cache_size, strategy='LIFO', random_seed=10)

cache

access_times

cache_size

strategy = 'LIFO'

random_seed = 10

initialized = False

get(key)

Retrieve an item from the cache. Updates access time for LRU/MRU.

put(key, value)

Add an item to the cache. Evicts an entry if the cache is full.

initialize_cache(func, path_onto, third, All_individuals, handle_restriction_func, concepts)

Initialize the cache with precomputed results for OWLClass and Existential concepts. :param ontology: The loaded ontology. :param func: Function to retrieve individuals for a given expression. :param concepts: List of OWL concepts to precompute and store instances for.

get_all_items()

is_full()

Check if the cache is full.

ontolearn.semantic_caching.semantic_caching_size(func, cache_size, eviction_strategy, random_seed, cache_type, concepts)

This function implements the semantic caching algorithm for ALC concepts as presented in the paper

ontolearn.semantic_caching.non_semantic_caching_size(func, cache_size)

This function implements a caching algorithm for ALC concepts without semantics.

ontolearn.semantic_caching.retrieve(expression: str, path_kg: str, path_kge_model: str) → Tuple[Set[str], Set[str]]

Retrieve instances with neural reasoner

ontolearn.semantic_caching.retrieve_other_reasoner(expression, path_kg, name_reasoner='HermiT')

Retrieve instances with symbolic reasoners

ontolearn.semantic_caching.run_semantic_cache(path_kg: str, path_kge: str, cache_size: int, name_reasoner: str, eviction: str, random_seed: int, cache_type: str, shuffle_concepts: str)

Return cache performnace with semantics

ontolearn.semantic_caching.run_non_semantic_cache(path_kg: str, path_kge: str, cache_size: int, name_reasoner: str, shuffle_concepts: str)

Return cache performnace without any semantics