Industrial Semantics-Aware Digital Twins: A Hybrid Graph Matching Approach for Asset Administration Shells
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911436434833408 |
|---|---|
| author | Metović, Ariana Maisch, Nicolai Ajdinović, Samed Lechler, Armin Wortmann, Andreas Riedel, Oliver |
| author_facet | Metović, Ariana Maisch, Nicolai Ajdinović, Samed Lechler, Armin Wortmann, Andreas Riedel, Oliver |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06613 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Industrial Semantics-Aware Digital Twins: A Hybrid Graph Matching Approach for Asset Administration Shells Metović, Ariana Maisch, Nicolai Ajdinović, Samed Lechler, Armin Wortmann, Andreas Riedel, Oliver Information Retrieval 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. |
| title | Industrial Semantics-Aware Digital Twins: A Hybrid Graph Matching Approach for Asset Administration Shells |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2601.06613 |