# DTAG: A Methodology for Aggregating Digital Twins Using the WOTDT Ontology
## 4 DTw Aggregation Methodology
### Ontology Aggregation  
The goal of the ontology aggregation activity is to perform an aggregation between two ontologies to obtain a single ontology that contains the conceptualisation needed for the DTwA data. This activity involves the DTwA user performing the aggregation process and the domain experts providing information about the ontologies domain. The output of this activity is an ontology resulting from the aggregation of the ontologies of the DTws involved in the aggregation process and its reference in the instantiation of the DTwA using WoTDT ontology. In the aggregation process of ontology models, multiple scenarios can be found. The scenarios depend on whether the ontology models of the DTws to be aggregated are (i) identical, (ii) partially overlapping, or (iii) completely distinct. For each of these scenarios, the aggregation steps are different. Scenarios that involve identical or partially overlapping ontologies do not require operational aggregation because these ontologies are interconnected. In the first scenario, the ontologies are identical, whereas in the second scenario, they are linked to each other. Therefore, DTws ontologies must be referenced by DTwA instantiation using WoTDT ontology. Meanwhile, in the third scenario, a process called ontology alignment needs to be performed. In Figure 13, the workflow of this activity is presented depending on the particular aggregation scenario. The ontology alignment process performed in the third scenario refers to the activity of identifying similarities between two or more ontologies and then exploiting or storing them [32; 42]. For these ontology alignment activities, there is an organisation called the Ontology Alignment Evaluation Initiative (OAEI) ([[https://oaei.ontologymatching.org/2023/](https://oaei.ontologymatching.org/2023/)]([https://oaei.ontologymatching.org/2023/](https://oaei.ontologymatching.org/2023/)), accessed on 18 May 2024), which annually proposes challenges to perform better ontology alignment algorithms. The OAEI initiatives are as follows: (i) evaluation of the advantages and disadvantages of alignment/matching systems; (ii) comparison of the performance of techniques; (iii) improvement of the level of communication among algorithm developers; (iv) improvement of evaluation techniques; (v) assistance in improving ontology alignment and matching research. In recent years, the Matcha and LogMap frameworks have been used the most for ontology alignment and matching in OAEI due to the fact that they offer the best results. On the one hand, Matcha [43] is an innovative ontology matching system that seeks to address persistent issues in the field of ontology matching. It integrates the essential algorithms of AgreementMakeLight (AML) [44] into a new core framework that is specifically designed to handle the matching of multiple ontologies and complex ontology matching. Furthermore, its structure is more flexible and scalable compared to that of AML. Additionally, Matcha includes an extension known as Matcha-DL, which is a variation of Matcha designed for semi-supervised learning. It uses the scores generated by the multiple matching algorithms in Matcha to train a dense neural network for the purpose of distinguishing and ranking a group of candidates identified by Matcha. The results of this framework for the OAEI challenge of last year can be seen in [45]. On the other hand, LogMap [46] is an ontology matching system known for its high scalability, incorporating reasoning and inconsistency repair features to generate mappings between classes, properties, and instances. Additionally, LogMap stands out as one of the few matching systems that can effectively align complex ontologies with a large number of classes (tens and hundreds of thousands of classes), utilising advanced reasoning and repair methods to reduce logical inconsistencies and offering user support options throughout the matching procedure. The results of this framework for the OAEI challenge of last year can be seen in [47].