Interoperability of semantically heterogeneous digital twins through Natural Language Processing methods

Abstract

Self-organizing systems represent the next stage in the development of automation technology. For being able to interact with each other in an interoperable manner, it requires a uniform digital representation of the system’s components, in the form of digital twins. In addition, the digital twins must be semantically interoperable in order to realize interoperability without the need for costly engineering in advance. For this purpose, the current research approach focuses on a semantically homogeneous language space. Due to the multitude of actors within an automation network, the agreement on a single semantic standard seems unlikely. Different standards and vendor-specific descriptions of asset information will continue to exist. This paper presents a method extending the homogeneous semantics approach to heterogeneous semantics. For this purpose, a translation mechanism is designed. The mapping of unknown vocabularies to a target vocabulary enables the interactions of semantically heterogeneous digital twins. The mapping is based on methods from the artifcial intelligence domain, specifically machine learning and natural language processing. Semantic attributes (name, definition) as well as further classifying attributes (unit, data type, qualifier, category, submodel element subtype) of the digital twins’ attributes are used therefore. For the mapping of the semantic attributes pre-trained language models on domain specific texts and sentence embeddings are combined. A decision tree classifies the other attributes. Different semantics for submodels of pumps and HVAC systems are used as the evaluation dataset. The combination of the classification of the attributes (decision tree) and the subsequent semantic matching (language model), leads to a significant increase in accuracy compared to previous studies.