Merge pull request #345 from dice-group/general_adjustments

tbox ,abox, triples methods and some general adjustments

Author: Demirrr

docs/usage/04_knowledge_base.md

@@ -253,11 +253,43 @@ You can now:
     print(evaluated_concept.ic) # 3
     ```
 
+## Obtaining axioms
+
+You can retrieve Tbox and Abox axioms by using `tbox` and `abox` methods respectively.
+Let us take them one at a time. The `tbox` method has 2 parameters, `entities` and `mode`.
+`entities` specifies the owl entity from which we want to obtain the Tbox axioms. It can be 
+a single entity, a `Iterable` of entities, or `None`. 
+
+The allowed types of entities are: 
+- OWLClass
+- OWLObjectProperty
+- OWLDataProperty
+
+Only the Tbox axioms related to the given entit-y/ies will be returned. If no entities are 
+passed, then it returns all the Tbox axioms.
+The second parameter `mode` _(str)_ sets the return format type. It can have the
+following values:
+1) `'native'` -> triples are represented as tuples of owlapy objects.
+2) `'iri'` -> triples are represented as tuples of IRIs as strings.
+3) `'axiom'` -> triples are represented as owlapy axioms.
+
+For the `abox` method the idea is similar. Instead of the parameter `entities`, there is the parameter 
+`individuals` which accepts an object of type OWLNamedIndividuals or Iterable[OWLNamedIndividuals].
+
+If you want to obtain all the axioms (Tbox + Abox) of the knowledge base, you can use the method `triples`. It
+requires only the `mode` parameter.
+
+> **NOTE**: The results of these methods are limited only to named and direct entities. 
+> That means that especially the axioms that contain anonymous owl objects (objects that don't have an IRI)
+> will not be part of the result set. For example, if there is a Tbox T={ C ⊑ (A ⊓ B), C ⊑ D }, 
+> only the latter subsumption axiom will be returned.
+
+
 -----------------------------------------------------------------------------------------------------
 
-See [KnowledgeBase API documentation](ontolearn.knowledge_base.KnowledgeBase)
-to check all the methods that this class has to offer. You will find methods to 
-access the class/property hierarchy, convenient methods that use the reasoner indirectly and 
+Since we cannot cover everything here in details, see [KnowledgeBase API documentation](ontolearn.knowledge_base.KnowledgeBase)
+to check all the methods that this class has to offer. You will find convenient methods to 
+access the class/property hierarchy, methods that use the reasoner indirectly and 
 a lot more.
 
 Speaking of the reasoner, it is important that an ontology 

main.py

@@ -18,6 +18,7 @@ def get_default_arguments(description=None):
                         default='NCESData/family/embeddings/ConEx_entity_embeddings.csv',
                         help="Path to knowledge base embeddings. Some models like NCES require this,"
                              "e.g. 'some/path/kb_embeddings.csv'")
+    parser.add_argument("--save", action="store_true", help="save the hypothesis?")
     # Common model arguments
     parser.add_argument("--path_learning_problem", type=str, default='examples/uncle_lp2.json',
                         help="Path to a .json file that contains 2 properties 'positive_examples' and "

ontolearn/__init__.py

@@ -16,5 +16,3 @@
 # from .metrics import *
 # from .search import *
 __all__ = ['knowledge_base', 'abstracts', 'base_concept_learner', 'metrics', 'search']
-
-from .learners import Drill

ontolearn/base/fast_instance_checker.py

@@ -6,8 +6,9 @@
 from functools import singledispatchmethod, reduce
 from itertools import repeat, chain
 from types import MappingProxyType, FunctionType
-from typing import DefaultDict, Iterable, Dict, Mapping, Set, Type, TypeVar, Optional, FrozenSet
+from typing import DefaultDict, Iterable, Dict, Mapping, Set, Type, TypeVar, Optional, FrozenSet, cast
 
+from ontolearn.base import OWLReasoner_Owlready2
 from ontolearn.base.ext import OWLReasonerEx
 from owlapy.model import OWLDataRange, OWLObjectOneOf, OWLOntology, OWLNamedIndividual, OWLClass, \
     OWLObjectProperty, OWLDataProperty, OWLObjectUnionOf, OWLObjectIntersectionOf, OWLObjectSomeValuesFrom, \
@@ -187,6 +188,12 @@ def disjoint_data_properties(self, dp: OWLDataProperty) -> Iterable[OWLDataPrope
     def sub_data_properties(self, dp: OWLDataProperty, direct: bool = False) -> Iterable[OWLDataProperty]:
         yield from self._base_reasoner.sub_data_properties(dp=dp, direct=direct)
 
+    def super_data_properties(self, dp: OWLDataProperty, direct: bool = False) -> Iterable[OWLDataProperty]:
+        yield from self._base_reasoner.super_data_properties(dp=dp, direct=direct)
+
+    def super_object_properties(self, op: OWLObjectProperty, direct: bool = False) -> Iterable[OWLDataProperty]:
+        yield from self._base_reasoner.super_object_properties(op=op, direct=direct)
+
     def sub_object_properties(self, op: OWLObjectPropertyExpression, direct: bool = False) \
             -> Iterable[OWLObjectPropertyExpression]:
         yield from self._base_reasoner.sub_object_properties(op=op, direct=direct)