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.

翻译：尽管资产管理壳（AAS）标准为工业资产提供了结构化且机器可读的表示形式，但其语义可比性仍然是一个重大挑战，尤其是在使用不同词汇表和建模实践的情况下。工程领域可通过检索与目标相似的历史AAS模型来复用子模型、参数及元数据。然而在实践中，异构的词汇体系与差异化的建模规范阻碍了跨AAS的自动化内容级比对。本文提出一种混合图匹配方法，以实现数字孪生表征的语义感知比较。该方法结合了基于SPARQL的规则预过滤与利用RDF2vec的嵌入相似度计算，以同时捕获AAS模型间的结构关联与语义关系。本研究成果为数字孪生网络中增强的发现机制、复用策略及自动化配置奠定了理论基础。