用混合图匹配技术实现工业数字孪生的语义级相似检索
Industrial Semantics-Aware Digital Twins: A Hybrid Graph Matching Approach for Asset Administration Shells
- 结合SPARQL规则过滤与RDF2vec嵌入计算，兼顾结构与语义匹配
- 支持跨异构词汇表的AAS模型相似性比较，提升复用效率
- 适合工业数字孪生系统开发与资产配置自动化场景
尽管资产行政壳（AAS）标准提供了工业资产的结构化、机器可读表示，但其语义可比性仍是重大挑战，尤其在使用不同词汇表和建模实践时。工程应用若能检索到与目标相似的现有AAS模型，便可复用子模型、参数和元数据。然而，异构词汇表和建模惯例阻碍了AAS间内容级的自动化比较。本文提出一种混合图匹配方法，实现数字孪生表示的语义感知比较。该方法结合基于规则的预过滤（SPARQL）与基于嵌入的相似性计算（RDF2vec），以捕捉AAS模型间的结构与语义关系。本工作为数字孪生网络中的发现、复用与自动化配置提供了基础。
原文摘要 · Abstract (English)
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.
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