# DTAG: A Methodology for Aggregating Digital Twins Using the WOTDT Ontology
## 5 Conclusions  
DTws are a powerful solution that enhances the accuracy of needed predictions to be improved, making more logical decisions and developing well-informed plans. However, there is no standard architecture that provides a precise definition of a DTw and offers guidance for its development. In the literature, numerous research proposals describe their architecture using a three-dimensional model [7]. Subsequently, a new proposal introduced an expanded model of DTw in five dimensions [8], enhancing the comprehension of the DTws architectures. However, DTws lack formal semantics, hindering their interoperability with external third-party DTws or services. To address this issue, the WoTDT ontology has been developed as an extension of the Thing Description ontology suggested by the W3C Web of Things group. This standard facilitates the description of services and enhances interoperability, discoverability, accessibility, security, and scalability. Consequently, this ontology will enable a more accurate understanding of DTws and will provide direct access to all system functionalities for the specified DTw. Moreover, even though the examples used in the article focus on the DTw construction domain, since the model used to conceptualise the DTw architecture (five-dimensional model [8]) is applied for generic DTws, this conceptualisation can be applied to DTws from different domains. Therefore, thanks to the reuse of ontologies in WoTDT, such as DCAT and W3C WoT Thing Description, which have not been developed for a particular domain, the WoTDT ontology has the flexibility to conceptualise DTws belonging to different domains. This can be done by extending the ontology by incorporating new additional subclasses into classes related to different dimensions, e.g., the addition of subclasses to represent new non-semantic models, such as energy or simulation models. However, introducing these new requirements could lead to problems that might trigger changes in the ontology. Hence, an opportunity for future research lies in expanding the ontology to include diverse information from multiple domains. Furthermore, when dealing with the aggregation of multiple DTws, in the literature, there are numerous approaches, such as [PERSON] [3] and [PERSON]'s [14] proposals, but the method in which the authors propose an aggregation where multiple DTws are aggregated in a single one (DTwA) is not present. Therefore, this article presents, together with the WoTDT ontology, a novel approach in which the authors propose an aggregation methodology, called DTAG, for the aggregation of DTws, which considers whether the DTw contains semantics or not. DTAG follows the dimensional aggregation according to the five-dimensional model of [PERSON] and [PERSON], where WoTDT ontology is applied. Therefore, this approach, distinct from those aggregations presented in the literature, allows the aggregation of both metadata and data (whether semantic or non-semantic) from various DTws within a DTwE. This results in the creation of a DTwA that contains all metadata, centralised thanks to the WoTDT ontology, along with all data related to the DTwIs or DTwAs of the DTwE involved in the aggregation process. Furthermore, the aggregation method allows the creation of DTwE. In the case of Section 4, it shows the aggregation of two DTws of construction pilot sites in a DTwA of the construction-related project. An additional advantage of using the WoTDT ontology in the DTAG aggregation methodology is the enhanced comprehension of the aggregation data itself, i.e., where each DTw comes from and how the DTwA has reached the aggregated state. At the same time, there is no loss of specificity because DTws are not removed in the aggregation activity. Instead, a DTwA is created that includes them. Therefore, the DTws involved in the aggregation can still operate without the need to access them through the DTwA.