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Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT.

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Summary
This summary is machine-generated.

This study introduces a self-learning model for seamless device integration in Industry 4.0 and 5.0. It enables dynamic machine-to-machine translation, overcoming interoperability challenges in Industrial Internet of Things networks.

Keywords:
Industry 4.0Industry 5.0 IIoTM2M translationdeep learningindustrial internet of thingsknowledge graphontology alignmentself-attentionsmart city

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Area of Science:

  • Industrial Automation
  • Internet of Things (IoT)
  • Artificial Intelligence

Background:

  • Modern Industry 4.0 and 5.0 rely on interconnected devices for optimized resource management.
  • Industrial Internet of Things (IIoT) faces challenges with heterogeneous devices lacking compatible designs and interoperability.
  • Conventional solutions for device integration are costly and require extensive engineering effort.

Purpose of the Study:

  • To propose a self-learning model for determining device taxonomy and enabling seamless integration.
  • To address the challenge of integrating new devices with different ontologies into existing IoT networks.
  • To facilitate dynamic machine-to-machine (M2M) translation without additional engineering or hardware.

Main Methods:

  • Utilizing a self-learning model to analyze ontological meta-data and structural information of devices.
  • Employing Natural Language Processing (NLP) to match distinct ontologies based on linguistic contexts.
  • Visualizing ontological networks as knowledge graphs to understand meta-data structure and message formulation.
  • Aligning entities of ontological graphs with similar context and structure.

Main Results:

  • The model successfully determines device taxonomy and identifies matches between different ontologies.
  • It enables dynamic M2M translation, enhancing interoperability within IIoT networks.
  • The approach reduces the need for manual engineering and additional hardware resources for device integration.

Conclusions:

  • The proposed self-learning model offers an efficient and cost-effective solution for device interoperability in Industry 4.0/5.0.
  • It facilitates dynamic translation and integration of heterogeneous devices, crucial for smart cities and industrial automation.
  • This advancement supports the seamless expansion of IoT networks and optimizes energy distribution and resource management.