Automated generation of Asset Administration Shell: a transfer learning approach with neural language model and semantic fingerprints

Automated generation of Asset Administration Shell: a transfer learning approach with neural language model and semantic fingerprints

연구 분야: Networking

학회: 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)

The Asset Administration Shell (AAS) is a standardized data container for sharing data in the context of Industry 4.0. It allows different participants or systems to communicate their information based on shared meaning. Today, however, AAS makes data interoperable at the cost of extra development effort. Developers must map a proprietary information model to a standardized AAS model during the data transformation. In this work-in-process paper, a novel data transformation method based on transfer learning with neural language model is proposed to automatically map the data properties from an arbitrary information model into a standardized AAS model. The term "semantic fingerprint" is used to characterize a pivot intermediate vector generated by a neural network, containing latent conceptual meaning about a data property, which is in turn used for generating the mappings between data properties with similar conceptual meaning. By this means, the proposed approach fills the research gap on automated generation of AAS models with semantic analysis and is able to lower the barrier to adopting AAS with tool support.