ABox Enrichment Through Semantic Data Recognition in Industrial Internet of Things

As the internet of things networks tend to grow bigger and bigger, the need for interoperability across systems is increasing. However, due to privacy, security, and segmentation across companies, the current practice is to create always new data silos, leading to a discontinuity in the unity of data. To address this, ontologies are seeing increasing usage in the industrial world. Nevertheless, the problem of linking the data back to the ontology is still present, which is often done manually.This paper introduces an approach to simplify this task by providing a semi-supervised tool using semantic data type detection and deep learning. One of the features of the proposed method is the ability to train the model without the need to keep every dataset stored together.