3K: Knowledge-Enriched Digital Twin Framework

E. Karabulut, P. Groth, V. Degeler

Abstract: Digital Twins (DTs) are the digital equivalent of physical entities that facilitate, among others, monitoring and decision-making, thus helping extend the longevity of the twinned entity. DTs with automated decision-making capabilities require explainable inference mechanisms, especially for critical infrastructures such as water networks. Here we introduce 3K, a DT framework that aims for knowledge-enriched inference that is explainable and fast, by synthesizing knowledge representation (semantics) and knowledge discovery methods. 3K constructs a knowledge graph, which is becoming a mainstream way of metadata storage in DTs, and proposes a new method that can run on both sensor data and knowledge graphs to learn semantic association rules. The rules represent the expected working conditions of the DT and we argue that when combined with domain knowledge in the form of ontological axioms, semantic association rules can help perform downstream tasks in DTs, including extending the longevity of the twinned entities such as an Internet of Things (IoT) system. Furthermore, we demonstrate the 3K framework in a water distribution network use case and show how it can be used for downstream tasks.

External IDs:doi:10.1145/3703790.3703834