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Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-2123-8187
Department of Computer Science, National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot, Pakistan.ORCID iD: 0000-0002-0368-5039
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-5662-825X
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-4029-6574
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2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 20, article id 8427Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2023. Vol. 23, no 20, article id 8427
Keywords [en]
deep learning, industrial internet of things, Industry 4.0, Industry 5.0 IIoT, knowledge graph, M2M translation, ontology alignment, self-attention, smart city
National Category
Computer Sciences Communication Systems
Research subject
Machine Learning; Cyber-Physical Systems
Identifiers
URN: urn:nbn:se:ltu:diva-102316DOI: 10.3390/s23208427ISI: 001095200100001PubMedID: 37896522Scopus ID: 2-s2.0-85175279210OAI: oai:DiVA.org:ltu-102316DiVA, id: diva2:1809846
Note

Validerad;2023;Nivå 2;2023-11-14 (marisr);

License fulltext: CC BY

Available from: 2023-11-06 Created: 2023-11-06 Last updated: 2023-12-12Bibliographically approved

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Javed, SalehaSandin, FredrikLiwicki, MarcusMokayed, Hamam

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