# DTAG: A Methodology for Aggregating Digital Twins Using the WOTDT Ontology
## 2 State of the Art
Additionally, group DTws and system DTws, which are compilations of personal DTws organised by specific criteria, serve the purpose of training machine learning models. While personal DTws contain all relevant medical data of a patient, group and system DTws only gather the data necessary for the intended model, thereby excluding details like the patient's name to maintain privacy and anonymity. Furthermore, architectures for DTw aggregations have been proposed. [PERSON] et al. [19] proposed a hierarchical aggregation approach, where local DTws focused on monitoring and diagnosing the health of assets, whereas global DTws focused on decision-making processes. [PERSON] et al. [20] introduced a framework in which data from various DTw are organised into layers. These layers are subsequently aggregated into different collections tailored to the user's preferences and required level of detail. Finally, [PERSON] et al. [21] proposed a six-layer architecture for DTw with aggregation called SLADTA (six-layer architecture for DTws with aggregation), which is an extension of the SLADT framework that allows multiple DTws to aggregate data for a system-level perspective. **DTw aggregation problems and possible solutions:** From these aggregations between different DTws, numerous problems can be found. A complex system usually contains multiple subsystems and components that may have their own DTw models. These models may be created by various stakeholders based on different protocols and standards whose structures and data sources are often heterogeneous in terms of syntax, schema, and semantics. This makes the integration of DTw models a difficult task [4]. To solve this problem, in the literature, the use of semantic technologies has been used as key components in various DTws to obtain semantic interoperability in data and in heterogeneous information [5; 6]. **Semantics in DTw:** The application of semantic web technologies in the development, management, and modelling of DTws has grown in recent years. Specifically, recommendations have been proposed on how to integrate ontologies and their advantages for DTw [22]. The ontologies of the DTws are used to (i) improve the visualisation and understanding of the DTw data [23] and (ii) describe the architecture model of a DTw [24]. In the literature, various studies have been conducted concerning the description of an architecture model of a DTw, which is the goal of WoTDT ontology. [PERSON] et al. [24] explored the extension of IoT-Lite ontology [25] by adding classes related to the virtual aspect of the three-dimensional model, which enables external applications and systems to interact with the virtual dimension through a specific protocol. Following this approach, [PERSON] et al. [26] introduced an ontology designed to encapsulate domain knowledge and preserve the semantic integrity of asset functionalities and fundamental properties throughout their operational period within the virtual aspect. Furthermore, [PERSON] of Microsoft Azure introduced the digital twins definition language (DTDL) [27], which uses an RDF metamodel to represent DTw virtual aspect metadata within their proprietary tools. **WoT in DTws:** In the literature, the research conducted by [PERSON] et al. [28] outlines the development of a DTws ecosystem that employs semantic web technologies within the healthcare domain. In their research, the integration of DTw and WoT improves the accuracy of real-time asset monitoring and interoperability with different systems. A different proposal by [PERSON] et al. explored the integration of the WoT standards with smart contracts to represent DTws of physical devices [29]. Furthermore, the study by [PERSON] and [PERSON] [30] introduced an architecture based on the WoT standard to facilitate a DTw in a smart environment involving sensors implemented in a construction-related project. Finally, [PERSON] et al. [4] integrated these technologies, applying WoT in cognitive DTws (CDTws) to standardise and facilitate data interoperability.