diff --git a/README.md b/README.md
index b13af48..d121e51 100644
--- a/README.md
+++ b/README.md
@@ -30,8 +30,9 @@ pytest -p no:warnings -x # Running 103 tests takes ~ 30 mins
 
 ## Usage
 
-In this example we start with a simple atomic class expression and move to some more complex 
-ones and finally render and print the last of them in description logics syntax.
+
+### Creating OWL Class Expressions
+<details><summary> Click me! </summary>
 
 ```python
 from owlapy.class_expression import OWLClass, OWLObjectIntersectionOf, OWLObjectSomeValuesFrom
@@ -40,17 +41,13 @@ from owlapy import owl_expression_to_sparql, owl_expression_to_dl
 
 # Create the male class
 male = OWLClass("http://example.com/society#male")
-
 # Create an object property using the iri as a string for 'hasChild' property.
 hasChild = OWLObjectProperty("http://example.com/society#hasChild")
-
 # Create an existential restrictions
 hasChild_male = OWLObjectSomeValuesFrom(hasChild, male)
-
 # Let's make it more complex by intersecting with another class
 teacher = OWLClass("http://example.com/society#teacher")
 teacher_that_hasChild_male = OWLObjectIntersectionOf([hasChild_male, teacher])
-
 # You can render and print owl class expressions in description logics syntax (and vice-versa)
 print(owl_expression_to_dl(teacher_that_hasChild_male))
 # (∃ hasChild.male) ⊓ teacher
@@ -68,7 +65,35 @@ class. In the above examples we have introduced 3 types of class expressions:
 Like we showed in this example, you can create all kinds of class expressions using the 
 OWL objects in [owlapy api](https://dice-group.github.io/owlapy/autoapi/owlapy/index.html).
 
-Many axioms can automatically inferred with a selected reasoner
+</details>
+
+### Logical Inference
+
+<details><summary> Click me! </summary>
+
+```python
+from owlapy.owl_ontology_manager import OntologyManager
+from owlapy.owlapi_adaptor import OWLAPIAdaptor
+
+ontology_path = "KGs/Family/family-benchmark_rich_background.owl"
+# Available OWL Reasoners: 'HermiT', 'Pellet', 'JFact', 'Openllet'
+owlapi_adaptor = OWLAPIAdaptor(path=ontology_path, name_reasoner="Pellet")
+onto = OntologyManager().load_ontology(ontology_path)
+# Iterate over defined owl Classes in the signature
+for i in onto.classes_in_signature():
+    # Performing type inference with Pellet
+    instances=owlapi_adaptor.instances(i,direct=False)
+    print(f"Class:{i}\t Num instances:{len(instances)}")
+owlapi_adaptor.stopJVM()
+```
+
+</details>
+
+### Ontology Enrichment
+
+<details><summary> Click me! </summary>
+
+An Ontology can be enriched by inferring many different axioms.
 ```python
 from owlapy.owlapi_adaptor import OWLAPIAdaptor
 
@@ -86,7 +111,10 @@ adaptor.infer_axioms_and_save(output_path="KGs/Family/inferred_family-benchmark_
 adaptor.stopJVM()
 ```
 
-Check also the [examples](https://github.com/dice-group/owlapy/tree/develop/examples) folder.
+</details>
+
+
+Check also the [examples](https://github.com/dice-group/owlapy/tree/develop/examples) and [tests](https://github.com/dice-group/owlapy/tree/develop/tests) folders.
 
 ## How to cite
 Currently, we are working on our manuscript describing our framework.