# Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
## 1 Introduction  
Ontologies are playing an increasingly important role in knowledge representation. In general, they provide a structured knowledge representation in which concepts are stored together with their properties and the relations between them [1]. However, ontologies can reflect inaccuracies in dynamic environments due to the fact that they are usually considered for environments with predictable behaviors. As a result, they are powerless to deal with unforeseen and new changes in the surroundings, since knowledge encapsulated in an ontology does not evolve over time. Thus, a considerable issue for using an ontology is the lack of support of a dynamic environment, in which changes are frequently emerging at runtime. To take into consideration dynamic environments, unforeseen changes have to be reflected in the ontologies at runtime. Therefore, ontologies must be used, and during their period of use, the knowledge on which they rely needs to be updated. Nevertheless, developing ontologies is deemed to be time-consuming and often expensive, but updatingthem is even more expensive, which usually constitutes the potential limit for addressing the rapid changes occurring in dynamic environments.  
One way to overcome the issue of irrelevant knowledge stored in ontologies within dynamic environments and the increasing costs associated with adapting them to environment changes is the use of an approach known as ontology learning. The ontology learning approach refers to the process of deriving relevant knowledge such as concepts, proprieties, and relations, as well as the occasional axioms from input data to build a new ontology or to enrich the preexisting one. That notwithstanding, ontology learning is not without issues due to the variety of changes that can arise in the enclosed environment at runtime. Hence, this cannot be manually performed effectively by experts, since they are not able to foresee all aspects of a change. Moreover, it also struggles to fully automate the learning process. Thus, most works in ontology learning require a minimum expert contribution and insight at runtime. For example, some works start building an ontology semi-automatically from scratch [2,3], requiring expert intervention at runtime. Furthermore, other works start enriching preexisting ontologies from scratch [4,5] based on expert contributions. To address these issues, we focus on proposing an automatic ontology-based model evolution approach to support dynamic environments at runtime through the evolution of an initial model expressed as an ontology. The aim of this proposed approach is twofold: (1) to analyze heterogeneous semi-structured input data for learning an ontology and (2) to use the learned ontology for extending and improving an initial ontology-based model to accommodate the changes that occur in its closed dynamic environment. The process of ontology-based model evolution is driven by the task of ontology learning and proceeds automatically with a non-expert contribution at runtime to deal with environment changes. In addition, we cover the implementation details and a case study to illustrate the proposed approach. Finally, we perform feature-based, criteria-based, and expert-based evaluation approaches to assess the quality of the proposed approach's results.  
The remainder of the paper is structured as follows. Section 2 discusses related work and highlights our contributions. In Section 3, we present in detail an ontology-based model evolution approach. Section 4 describes a proof-of-concept prototype implementation and a case study. A validation of the proposed approach is given through evolved ontology-based model evaluation, which is introduced in Section 5. Section 6 presents the evaluation results to demonstrate the potential of the evolved ontology-based models. In Section 7, we conduct discussions about the main results achieved. Finally, Section 8 concludes with directions toward future work.