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

2026-01-09

Consumer Communications and Networking Conference

AI Summary

This study addresses the challenge of semantic incomparability among Asset Administration Shell (AAS) models in industrial digital twins, caused by heterogeneous vocabularies and modeling conventions, which hinders their automated comparison and reuse. To overcome this limitation, the authors propose a hybrid graph matching approach that integrates rule-based reasoning with knowledge graph embeddings. Specifically, SPARQL-based rules are first applied to pre-filter candidates according to structural constraints, followed by semantic similarity computation using RDF2vec embeddings. This joint structure-semantic alignment strategy uniquely combines rule-driven filtering with embedding-based semantics, moving beyond traditional methods that rely solely on syntactic structure or keyword matching. The proposed method significantly enhances the discoverability, reusability, and automated configurability of AAS models in heterogeneous industrial environments.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Ontologies
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• Asset Administration Shell
• Semantic Comparability
• Digital Twin
• Heterogeneous Vocabularies
• Model Reuse

Innovation

• hybrid graph matching
• Asset Administration Shell
• semantic similarity
• RDF2vec
• Digital Twin