# Semantic Description of Explainable Machine Learning Workflows for Improving Trust
## 5 Conclusions  
In this paper, we propose an ontology to describe the main components of the ML process and post-hoc explanation process, providing means that enable a user to have a holistic understanding of why the ML model arrived at these specific results. In the literature, different ontologies for data mining and ML can be found, but currently, to our best knowledge, there is no ontology to represent ML and explanation processes that also aim to achieve explainability by focusing on a complete overview of all components of these processes. Our ontology was developed following the best practices by adhering to the SABiO methodology and explores state-of-the-art technologies for ontology engineering, for instance, the OntoUML plugin and the UFO plugin for Protege. The ontology is based on existing ontologies, but it also considers different points of view from domain experts. We aligned MLS, which is the main domain-specific ontology that inspired the development of our ontology, with the ML process and grounded it in a foundational ontology, making the ontology interoperable with existing ontologies that follow UFO. The process of grounding the domain ontology presented some challenges because it required the alignment of ontologies that follow different philosophies and deep knowledge of their concepts. OntoUML has been fundamental to facilitating the grounding process. However, it required the use of gUFO, lacking the support of UFO-C, which has elements that could be used in our ontology to represent, for example, actions and goals. After grounding MLS in gUFO, our ontology was structured using modules, and descriptors of each element considered necessary to describe the processes were added to the ontology. The ontology was then evaluated by using the OntoUML plugin for Visual Paradigm, which was used to build the conceptual model and also provided verification, showing that our ontology is sound. After the transformation of the conceptual model to the gUFO-based operational ontology, we used Protege to create individuals and validate the ontology. The reasoner and the UFO plugin available to Protege also assisted the evaluation process by verifying consistency and ensuring that the rules of our gUFO-based ontology were satisfied. A case study was defined by considering the scenario of the COVID-19 pandemic, training an SVM model with data of infected patients to predict the mortality, and applying existing explanation methods to get feature correlations among the training data. We also applied post-hoc explanation methods to generate explanations concerning the behavior of the ML model, generating rules with RIPPER and obtaining the impact of each variable on the result with LIME. With the application of these methods, we obtained different explanations of the data and the trained ML model, which helped us better understand the logic behind it. The ontology was then populated with instances that describe the case study, which helped identify necessary changes. This also enabled the ontology to be queried, retrieving information for each CQ that ensured that the ontology fulfilled its intended purpose, leveraging the post-hoc explainability. Our ontology has been designed to enable the description of different kinds of ML experiments, with different ML algorithms, and it is modularized to enable the general ML  
Figure 5: SPARQL query for CQ1 for the first experiment. module to be extended and used for other purposes. It also aims to enable the description of different post-hoc explanation methods. The information that describes ML algorithms, explanation methods, metrics, etc., is expected to be easily reused. Furthermore, making the logic behind the ML model and the whole ML and explanation process clearer can help to ensure better understandability and trust. This is needed in many situations, such as the case study explored by this paper, which worked with private and sensitive patients' data and used the ML to predict the severity of a disease.