# Digital Twin-Enabled Decision Support Services in Industrial Ecosystems
## 4 Results
### Ontology and Semantic Modeling for Data Integration in the Hierarchical Concept  
As discussed in Section 4.2, across the entire lifecycle, the digital twins are used by different stakeholders under several underlying hierarchy levels across different enterprises, even up to the entire industrial ecosystem. The entities and their interrelationships representing the different digital twins in an ecosystem are formalized by knowledge graph models which are developed based on the defined ontology concepts. The ontology is defined as: \"an explicit, formal specification of a shared conceptualization of a domain of interest\" [40], which provides a formal description of domain knowledge exploited in different ways. If a unified ontology is used for formalizing the domain-specific knowledge across the entire ecosystem [41], such knowledge can be reused efficiently without waste of R&D costs led by non-shared knowledge. Therefore, it is important to design a unified ontology for many knowledge-intensive applications particularly for supporting digital twin integration. In order to support ontology lifecycle management, ontology engineering is proposed as a general discipline to provide methodologies and methods for building ontologies. It refers to \"The set of activities that concern the ontology development and the ontology lifecycle, the methods and methodologies for building ontologies and the tool suites and languages that support them\" [31]. The ontology engineering provides a guideline to define specific knowledge formalism with efficient reuse. Moreover, through such ontology, digital twins including virtual twins and their related systems can be described completely. It is the base to develop knowledge graph models for developing a seamless Information Technology (IT) infrastructure to operate with interoperability and standardization. Across the specific domains which are required to develop digital twins, ontology enables representing the nature of organizations, systems, systems engineering perspectives, system lifecycle, and virtual models of digital twins in the way of philosophy:  
* Based on ontology, semantic modeling can define entities and their interrelationships, which refers to digital twin related concepts and their communications. Based on the entire ontology, entities and their topologies, digital twins, are described by the taxonomy of the defined classes and object properties [42]. * System models and information models based on ontology provide the ability to abstract different kinds of data in order to promote the understanding that the data are related, with good scalability. * A semantic model refers to one type of information model describing digital twin entities and their topologies through structuring formalisms. A knowledge graph is one visualized representation of the specific semantic models using a semantic triple. Through a knowledge graph, semantic reasoning enables inference of logical consequences from a set of asserted facts or axioms. Thus, IT platforms can implement data analysis based on knowledge graph models through semantic query capabilities when managing the digital twins. As shown in Figure 5, in order to manage digital twin models through developed ontology, semantic modeling enables system designers to define the meaning and context of the digital twins and the related data and to formalize the domain-specific knowledge of the physical entities of the digital twins in an abstract way. Through exploiting semantic models for digital twin management and application, several benefits are found:  
* Avoiding misunderstanding of digital twins: through providing a clear, accessible, and agreed set of entities, and their relations as a trusted source and discussion, misunderstandings can easily be resolved. * Conducting decision making through reasoning: by being machine understandable and through the usage of logic statements (rules), ontologies enable automatic reasoning and inference, which leads to automatic generation of new and implicit knowledge for decision making.