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Book Sections Year : 2018

High-Level Knowledge Representation and Reasoning in a Cognitive IoT/WoT Context

Gian Piero Zarri
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Abstract

This chapter presents an overview of the Generalized World Entities (GWEs) paradigm, used to add a semantic/conceptual dimension to the ordinary IoT/WoT procedures. Its purpose is to expand the range of entities to be considered when describing a sensor-monitored environment by allowing, in particular, to seamlessly model in a unified way (i.e., within the same representation framework) physical entities like objects, humans, robots, etc. and higher levels of abstraction structures corresponding to general situations/actions/events/behaviours. The unifying factor is provided by the conceptual representation of the world used for modelling the GWEs of both types. This is ontology-based and general enough to take into account both the “static” (background information about, e.g., common notions like robot, person or physical object) and the “dynamic” (foreground information concerning, e.g., a robot or a person moving in real time towards a given object) characteristics of the different entities to deal with. After having presented a short state of the art in the cognitive/semantic IoT/WoT domain, we will specify the notion of GWE by describing its implementation under NKRL (Narrative Knowledge Representation Language) format. NKRL is a high-level modelling language, whose main characteristic concerns the use of two ontologies, an ontology of standard concepts and an ontology of events, this last dealing with the representation of the dynamic and spatio-temporal characterized information denoting behaviours, complex events, situations, circumstances etc. We will show, using several examples, that this dichotomy allows us to effectively model, in a seamlessly way, all the different entities managed by the usual IoT/WoT procedures.
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hal-03960590 , version 1 (27-01-2023)

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Gian Piero Zarri. High-Level Knowledge Representation and Reasoning in a Cognitive IoT/WoT Context. Cognitive Computing for Big Data Systems Over IoT, 14, Springer International Publishing, pp.223-262, 2018, Lecture Notes on Data Engineering and Communications Technologies, ⟨10.1007/978-3-319-70688-7_10⟩. ⟨hal-03960590⟩
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