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The SemIoE Ontology: A Semantic Model Solution for an IoE-based Industry

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arxiv 2401.06667 v1 pith:A3A6Q5WC submitted 2024-01-12 cs.IR

classification cs.IR
keywords industrydatamodelsemanticattentiondesigninternetioe-based
verification ladder T0 review T1 audit T2 compute T3 formal
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Recently, the Industry 5.0 is gaining attention as a novel paradigm, defining the next concrete steps toward more and more intelligent, green-aware and user-centric digital systems. In an era in which smart devices typically adopted in the industry domain are more and more sophisticated and autonomous, the Internet of Things and its evolution, known as the Internet of Everything (IoE, for short), involving also people, robots, processes and data in the network, represent the main driver to allow industries to put the experiences and needs of human beings at the center of their ecosystems. However, due to the extreme heterogeneity of the involved entities, their intrinsic need and capability to cooperate, and the aim to adapt to a dynamic user-centric context, special attention is required for the integration and processing of the data produced by such an IoE. This is the objective of the present paper, in which we propose a novel semantic model that formalizes the fundamental actors, elements and information of an IoE, along with their relationships. In our design, we focus on state-of-the-art design principles, in particular reuse, and abstraction, to build ``SemIoE'', a lightweight ontology inheriting and extending concepts from well-known and consolidated reference ontologies. The defined semantic layer represents a core data model that can be extended to embrace any modern industrial scenario. It represents the base of an IoE Knowledge Graph, on top of which, as an additional contribution, we analyze and define some essential services for an IoE-based industry.

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  1. Visualization of Knowledge Graphs with Embeddings: an Essay on Recent Trends and Methods

    cs.IR 2024-11 conditional novelty 3.0 of 10

    A survey of Knowledge Graph visualization tools finds that intuitive UI and performance are widely met but modularity is rare, with embedding-based tools splitting into exploratory and explanatory perspectives.

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