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Issue title: Internet of Things
Guest editors: Michele Ruta
Article type: Research Article
Authors: Ruta, Michele; * | Scioscia, Floriano | Loseto, Giuseppe | Pinto, Agnese | Di Sciascio, Eugenio
Affiliations: Department of Electrical and Information Engineering, Polytechnic University of Bari, via E. Orabona 4, I-70125, Bari, Italy
Correspondence: [*] Corresponding author. E-mail: [email protected].
Abstract: Novel Internet of Things (IoT) applications and services rely on an intelligent understanding of the environment leveraging data gathered via heterogeneous sensors and micro-devices. Though increasingly effective, Machine Learning (ML) techniques generally do not go beyond classification of events with opaque labels, lacking machine-understandable representation and explanation of taxonomies. This paper proposes a framework for semantic-enhanced data mining on sensor streams, amenable to resource-constrained pervasive contexts. It merges an ontology-based characterization of data distributions with non-standard reasoning for a fine-grained event detection. The typical classification problem of ML is treated as a resource discovery by exploiting semantic matchmaking. Outputs of classification are endowed with computer-processable descriptions in standard Semantic Web languages, while explanation of matchmaking outcomes motivates confidence on results. A case study on road and traffic analysis has allowed to validate the proposal and achieve an assessment with respect to state-of-the-art ML algorithms.
Keywords: Semantic Web, machine learning, non-standard reasoning, Internet of Things
DOI: 10.3233/SW-180314
Journal: Semantic Web, vol. 10, no. 1, pp. 183-204, 2019
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