VISOOnto: Semantic and Context-Aware Socialization of Objects in the Social Internet of Things based on Digital Twins

• Leandro Camargo IFSul
• Paulo Braga UFLA
• Felipe O. Silva UFLA
• Ana Marilza Pernas UFPel
• Adenauer Yamin UFPel

DOI: https://doi.org/10.5753/webmedia.2026.30167

Abstract

The Social Internet of Things (SIoT) enables heterogeneous devices to autonomously establish social relationships in intelligent environments. Existing relationship recommendation approaches, however, rely predominantly on structural interaction analysis and trust propagation, providing limited support for contextual interpretation and compatibility assessment. This work presents VISOOnto, a semantic-enhanced approach that integrates ontology-based inference, Digital Twins, Organizational Network Analysis (ONA), and graph-based interaction analysis to support context-aware relationship recommendation in SIoT environments. Extending the VISO ecosystem, VISOOnto employs OWL reasoning and SWRL rules to derive compatibility evidence from contextual conditions, device capabilities, operational constraints, trust attributes, and organizational roles, allowing semantic knowledge to actively influence recommendation decisions. VISOOnto was evaluated in a real smart office environment composed of heterogeneous devices operating under continuous interaction conditions. Comparative experiments against VISO demonstrated improvements of 25.3% in Semantic Recommendation Precision and 22.9% in Contextual Consistency Rate, with statistically significant gains in recommendation quality and contextual compatibility. The results demonstrate that semantic inference modifies recommendation outcomes beyond what can be achieved through structural interaction analysis alone. By transforming ontologies from passive knowledge representations into active decision mechanisms, VISOOnto advances adaptive and context-aware relationship management in heterogeneous SIoT environments.

Keywords: Social Internet of Things, Digital Twins, Semantic Web, Ontologies, Relationship Recommendation

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