This ontology is based on explainable classifier, which was built based on black-box Neural Network (NN) classifier trained on Iris dataset (https://www.kaggle.com/datasets/uciml/iris ). The trained NN was considered as a black-box classifier. We designed an algorithm, which: uses NN to generate syntetic samples; these samples were used to train a Decision Tree (we limit depth for the tree by 4); our algorithm automatically translate the decision tree rules into this ontology with SWRL rules for classifying test samples. Article describing all these in detailed is submitted to ISM-2025 Coference and can be referenced as follows: TO BE INCLUDED LATER. Python/Pythorch code is available here: TO BE INCLUDED. See below the trained Decision Tree classifier (tha basis for the rules): Extracted Decision Tree Rules: |--- PW ≤ 1.44 | |--- SW ≤ 3.06 | | |--- SL ≤ 5.08 | | | |--- PL ≤ 3.40 | | | | |--- Setosa (Rule 1) | | | |--- PL > 3.40 | | | | |--- Versicolor (Rule 2) | | |--- SL > 5.08 | | | |--- PL ≤ 5.86 | | | | |--- Versicolor (Rule 3) | | | |--- PL > 5.86 | | | | |--- Virginica (Rule 4) | |--- SW > 3.06 | | |--- SL ≤ 6.06 | | | |--- PL ≤ 5.86 | | | | |--- Setosa (Rule 5) | | | |--- PL > 5.86 | | | | |--- Setosa (Rule 6) | | |--- SL > 6.06 | | | |--- SW ≤ 4.09 | | | | |--- Versicolor (Rule 7) | | | |--- SW > 4.09 | | | | |--- Setosa (Rule 8) |--- PW > 1.44 | |--- PL ≤ 4.08 | | |--- SL ≤ 6.25 | | | |--- SW ≤ 3.09 | | | | |--- Virginica (Rule 9) | | | |--- SW > 3.09 | | | | |--- Setosa (Rule 10) | | |--- SL > 6.25 | | | |--- PW ≤ 2.40 | | | | |--- Versicolor (Rule 11) | | | |--- PW > 2.40 | | | | |--- Virginica (Rule 12) | |--- PL > 4.08 | | |--- SW ≤ 3.77 | | | |--- SW ≤ 3.19 | | | | |--- Virginica (Rule 13) | | | |--- SW > 3.19 | | | | |--- Virginica (Rule 14) | | |--- SW > 3.77 | | | |--- PW ≤ 2.04 | | | | |--- Setosa (Rule 15) | | | |--- PW > 2.04 | | | | |--- Virginica (Rule 16) Name Mappings: f0 (Decision tree); hasF0 (SWRL rules); sepal length (cm) (name of attribute in the original dataset); f1 (Decision tree); hasF1 (SWRL rules); sepal width (cm) (name of attribute in the original dataset); f2 (Decision tree); hasF2 (SWRL rules); petal length (cm) (name of attribute in the original dataset); f3 (Decision tree); hasF3 (SWRL rules); petal width (cm) (name of attribute in the original dataset); class: 0 (Decision tree); Class_0 (SWRL rules); setosa (name of class in the original dataset); class: 1 (Decision tree); Class_1 (SWRL rules); versicolor (name of class in the original dataset); class: 2 (Decision tree); Class_2 (SWRL rules); virginica (name of class in the original dataset).