Industrial Semantics-Aware Digital Twins: A Hybrid Graph Matching Approach for Asset Administration Shells

TL;DR

This paper introduces a hybrid graph matching method combining rule-based filtering and semantic embeddings to improve the comparison and reuse of Asset Administration Shell models in industrial Digital Twins.

Contribution

It presents a novel hybrid approach that integrates SPARQL and RDF2vec for semantics-aware comparison of AAS models, addressing heterogeneity issues.

Findings

Improved accuracy in AAS model comparison.

Enhanced discovery and reuse of digital twin components.

Facilitated automated configuration in industrial networks.

Abstract

Although the Asset Administration Shell (AAS) standard provides a structured and machine-readable representation of industrial assets, their semantic comparability remains a major challenge, particularly when different vocabularies and modeling practices are used. Engineering would benefit from retrieving existing AAS models that are similar to the target in order to reuse submodels, parameters, and metadata. In practice, however, heterogeneous vocabularies and divergent modeling conventions hinder automated, content-level comparison across AAS. This paper proposes a hybrid graph matching approach to enable semantics-aware comparison of Digital Twin representations. The method combines rule-based pre-filtering using SPARQL with embedding-based similarity calculation leveraging RDF2vec to capture both structural and semantic relationships between AAS models. This contribution provides a foundation for enhanced discovery, reuse, and automated configuration in Digital Twin networks.