Shifting from UML to ontology-based systems for enhanced digital twin integration

Shifting from UML to ontology-based systems for enhanced digital twin integration

During our “Leveraging AI* for built asset Digital Twins – Turning scattered data into strategic assets” webinar, we discussed the strategic shift from Unified Modeling Language (UML) to ontologies, highlighting this as a pivotal evolution in our approach to data management, particularly in the context of built asset digital twins.

Unified Modeling Language (UML): UML has been a standard modelling language used in software engineering to visualize the design of a system. It primarily operates under a ‘closed world assumption’, where any data not explicitly defined in the model is considered false. This approach, while structured, can be restrictive as it limits the ability to integrate new, unknown data into the existing model.

Transitioning to ontology-based system design:

Open world assumption: In contrast to UML, ontologies operate under an ‘open world assumption’. This paradigm shift means that unknown data is treated as just that—unknown—rather than false. This approach opens up new possibilities for integrating external data, allowing for a more dynamic and flexible data model.

Enhanced data linking and integration: By using ontologies, we enable the integration of diverse data sets, facilitating the connection and interoperability between different domains of knowledge. This is especially beneficial in complex fields like built asset management, where data from various sources needs to be combined and analyzed cohesively.

Scalability and evolution: Ontologies offer a scalable framework that can evolve with emerging data and concepts. This adaptability is crucial in today’s fast-paced digital environment, where new data types and relationships can emerge rapidly.

Benefits of the shift:

Richer data modelling: Ontologies allow for a more nuanced and comprehensive data representation. This richness is crucial in capturing the complex relationships and dependencies in built-in digital twins.

Greater flexibility and insight: The shift to ontologies provides a more flexible framework for data analysis, offering deeper insights and understanding of the data. It enables us to create more intelligent and responsive models that adapt to new information and changing conditions.

Facilitating knowledge graphs: The use of ontologies is instrumental in developing knowledge graphs, which are a key part of our strategy in managing and leveraging data. These graphs provide a powerful tool for visualizing and interacting with data, uncovering new insights and relationships.

Eurostep’s transition from UML to ontologies marks a significant advancement in our data strategy, aligning with our commitment to innovation and excellence in data management. This shift enhances our capability to manage complex data systems and aligns with our broader goal of transforming scattered data into strategic assets.

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