Modeling and Validating Temporal Rules with Semantic Petri-Net for Digital Twins

TL;DR

This paper introduces Semantic Petri-Net, a novel method for modeling and validating temporal rules in digital twins by integrating semantic and temporal models for dynamic rule checking.

Contribution

It presents a new approach using Semantic Petri-Net to enable real-time, bidirectional knowledge sharing between semantic web data and temporal models in digital twins.

Findings

Demonstrates application in digital twins with concurrent state changes.

Shows effective integration of semantic and temporal models.

Validates the approach with practical case studies.

Abstract

Semantic rule checking on RDFS/OWL data has been widely used in the construction industry. At present, semantic rule checking is mainly performed on static models. There are still challenges in integrating temporal models and semantic models for combined rule checking. In this paper, Semantic Petri-Net (SPN) is proposed as a novel temporal modeling and validating method, which implements the states and transitions of the Colored Petri-Net directly based on RDFS and SPARQL, and realizes two-way sharing of knowledge between domain semantic webs and temporal models in the runtime. Several cases are provided to demonstrate the possible applications in digital twins with concurrent state changes and dependencies.