This position paper proposes a platform for the creation of digital twins (DT) for social networks. These are mainly aimed at simulating human behavior from a cognitive point of view. The proposal relies on a semantic data infrastructure aimed at analytical purposes, which is directly fed with real data from social networks. Summarized data and data generation methods are then combined to produce new data streams according to the analyst requirements. All these data are stored in a dynamic knowledge graph, which plays a central role in the design of the DTs. First experiments will be conducted on two scenarios where semantic data is already available, namely: Tourism and Fashion.

Towards Semantic DigitalTwins for Social Networks

The field of Materials Science is concerned with, e.g., properties and performance of materials. An important class of materials are crystalline materials that usually contain "dislocation" -- a line-like defect type. Dislocation decisively determine many important materials properties. Over the past decades, significant effort was put into understanding dislocation behavior across different length scales both with experimental characterization techniques as well as with simulations. However, for describing such dislocation structures there is still a lack of a common standard to represent and to connect dislocation domain knowledge across different but related communities. An ontology offers a common foundation to enable knowledge representation and data interoperability, which are important components to establish a "digital twin". This paper outlines the first steps towards the design of an ontology in the dislocation domain and shows a connection with the already existing ontologies in the materials science and engineering domain.

Steps towards a Dislocation Ontology for Crystalline Materials

Digital twins are largely used to enable the Industry 4.0 and other Internet of Things applications. This approach has been built to support the product entire lifecycle and has advantages such as interoperability, availability, and simulation capabilities. If integrated with distributed ledger technologies, such as blockchains, digital twins can augment their applications, becoming an anchor of trust for constrained IoT devices. This paper describes the Constrained Devices Management Platform, a BT research system designed to enhance the security of IoT systems, with an especial focus on data provenance, immutability, and integrity. Lastly, the paper describes how semantics can enhance the CDMP capabilities through interoperability.

Semantic Digital Twins: security and scale for constrained IoT devices

Semantic-based Digital Twins can be applied to the enactment of the digital counterparts of enterprises and organizations, and not only for physical devices or machines. Digital Twins for Organizations have a profound impact in the architecture of corporate management platforms, in the design of business processes and in the conception of information and technological ecosystems.. This work condenses our experience in the creation of Digital Twins for Organizations using model-driven engineering based on semantic modeling. We will describe their more relevant characterizations and discuss their potential impact in their business implementations.

Semantic Digital Twins for Organizational Development

Offsite Manufacturing (OSM) is a modern and innovative method of construction with the potential to adopt advanced factory production system through a more structured workflow, standardised products, and the use of robotics for automation. However, there have been challenges in quantifying improvements from the conventional method, which leads to the low uptake. The concept of a digital twin (DT) is useful for OSM, which enables production to be represented virtually and visually including all activities associated with it, resources, and workflow involved. Thus, essential information in the product development process such as cost, time, waste, and environmental impacts can be assessed. However, the data required to have accurate results and better-informed decision-making come from heterogeneous data formats (i.e. spreadsheets and BIM models) and across different domains. The inclusion of semantic web technologies such as Linked Data (LD) and Web Ontology Language (OWL) models has proven to better address these challenges especially in terms of interoperability and unambiguous knowledge systematisation. Through an extensive systematic literature review followed up by a case study, an ontology knowledge structure representing the production workflow for OSM is developed. A real-life use case of a semi-automated production line of wall panel production is used to test and demonstrate the benefits of the semantic digital twin in obtaining cost and time data of the manufacturing for assessment. Results demonstrated the potential capability and power of capturing knowledge for an ontology to assess production workflow in terms of cost, time, carbon footprint thereby enabling more informed decision making for continuous improvements.

ESWC 2021 Workshop SeDiT Submissions

Towards Semantic DigitalTwins for Social Networks

Steps towards a Dislocation Ontology for Crystalline Materials

Semantic Digital Twins: security and scale for constrained IoT devices

Semantic Digital Twins for Organizational Development

A Semantic Offsite Construction Digital Twin- Offsite Manufacturing Production Workflow (OPW) Ontology