# Semantic Description of Explainable Machine Learning Workflows for Improving Trust
## 2 Ontology Concepts
### Ontology Specification
#### 2.1.2 Knowledge Acquisition and Reuse  
Knowledge Acquisition and Reuse are auxiliary processes defined in SABiO that assist ontology development [13]. Usually, Knowledge Acquisition occurs in the initial stages of ontology development to gather knowledge from different sources, while Reuse can be adopted in many opportunities to reuse already established conceptualizations.  
This paper applies Reuse in the Knowledge Acquisition process by selecting an already existing domain ontology and a foundational ontology. First, the ML process and the post-hoc explanation process are defined with their components and what should be described, as represented in Figure 2.  
The Machine Learning Process: This process consists of preprocessing data, training, and testing the ML model. The preprocessed data determine the preprocessing steps, such as the cleaning process, feature extraction or dimensionality reduction methods applied with respective parameters, and the criteria to split training and testing data. Given the different nature and particularities of the available datasets, which may require diverse preprocessing steps to make it adequate for the ML, we assume that the data have already been preprocessed so that only the preprocessing steps taken are modeled in the ontology, without more details.  
Training the ML model involves the training data, the ML implementation, the ML model, and the output. The training data represent the input data used to train the ML model. The ML implementation indicates the type and characteristics of the implemented algorithm, for example, Support Vector Machine (SVM) with the parameters used to train the model. The ML model (learned model) can be explained through the logic of the reasoning behind the decision-making process in general [7], which enables the user to understand the logic of the ML algorithm and the patterns observed by the ML model in the data. The description of the outputs obtained from the ML model is related to the explanation of a decision which refers to reasons that justify why a particular outcome was generated by the ML model.  
Testing the ML model comprises the descriptions of the testing data and the ML model evaluation. The description of the evaluation is relevant because many metrics can be used to evaluate the ML models. Choosing the best metrics depends on the task that the ML model is expected to perform and its application. Evaluation descriptions provide information about the metrics and how the ML model performs with respect to these metrics.  
The Post-hoc Explanation Process: This process comprises the explanation method, the explanation generated by the method, and the explainability evaluation. The method usually receives the output data from the ML model, and some of the methods also use the input training data to generate the explanation. The description of the post-hoc method indicates the method adopted and its characteristics; for instance, the scope of the method (local or global explanations), the format of the explanation it generates (tree, rules, decision table, images, text highlight, natural language, etc.), and whether the explanation is iterative or static. The explanation gives the logical reasoning behind the decision-making process, the patterns observed in the data, or provides means to justify the ML output. The evaluation contains information about the assessment of the explanations which can be evaluated in terms of effectiveness and user experience, such as the number of instances that the rules cover, and how faithful the explanations to the underlying black-box are.  
Figure 2: ML process with a training sub-process (black arrows), a testing sub-process (orange arrows), and a post-hoc explanation sub-process (blue arrows).