Leveraging AI for Enhanced Semantic Interoperability in IoT: Insights from NER Models

In Industry 4.0, achieving semantic interoperability is a significant problem due to the complexities of current automation systems and the numerous standards involved. The study explores how Artificial Intelligence (AI) and semantic interoperability connect within the Internet of Things (IoT) framework to overcome barriers to technology adoption. The main goal is to analyze how AI’s adaptive and predictive abilities might transform semantic interoperability by studying AI-driven methodologies to provide a flexible and efficient solution. The main objective of the paper is to leverage Named Entity Recognition (NER) AI models to streamline the identification of entities within the Internet of Things (IoT) for achieving semantic interoperability. It tests a Natural Language Processing (NLP) translator on data representations not seen during training, and the outcome highlights the efficiency of NLP in correctly understanding and processing these representations.