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

Sensors (Basel, Switzerland), 2023-10, Vol.23 (20), p.8427 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 by the authors. 2023 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s23208427 ;PMID: 37896522

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  • Title:
    Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT
  • Author: Javed, Saleha ; Usman, Muhammad ; Sandin, Fredrik ; Liwicki, Marcus ; Mokayed, Hamam
  • Subjects: Automation ; Computational linguistics ; Cyber-Physical Systems ; Cyberfysiska system ; deep learning ; Digitization ; industrial internet of things ; Industry 4.0 ; Industry 5.0 IIoT ; Internet of Things ; knowledge graph ; Language processing ; M2M translation ; Machine Learning ; Maskininlärning ; Natural language interfaces ; Ontology ; ontology alignment ; self-attention ; smart city ; Translating and interpreting
  • Is Part Of: Sensors (Basel, Switzerland), 2023-10, Vol.23 (20), p.8427
  • Description: The technical capabilities of modern Industry 4.0 and Industry 5.0 are vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnection and communication among heterogeneous devices. Smart cities are established with sophisticated designs and control of seamless machine-to-machine (M2M) communication, to optimize resources, costs, performance, and energy distributions. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, this encompasses quite a few challenges for devices that lack a compatible and interoperable design. The conventional solutions are restricted to limited domains or rely on engineers designing and deploying translators for each pair of ontologies. This is a costly process in terms of engineering effort and computational resources. An issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontological meta-data and structural information. The model finds matches between two distinct ontologies using a natural language processing (NLP) approach to learn linguistic contexts. Then, by visualizing the ontological network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device’s message formulation. Finally, the model can align entities of ontological graphs that are similar in context and structure.Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware resources.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s23208427
    PMID: 37896522
  • Source: Open Access: PubMed Central
    DOAJ Directory of Open Access Journals
    Geneva Foundation Free Medical Journals at publisher websites
    AUTh Library subscriptions: ProQuest Central
    SWEPUB Freely available online
    ROAD: Directory of Open Access Scholarly Resources
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