This paper proposes the object (b)logging framework, a novel general approach for the Semantic Web of Things. It allows to associate semantic annotations to real-world objects and events as well as to trigger complex objects interactions through advanced resource discovery. Machine learning algorithm are combined with non-standard reasoning services in order to produce a rich and meaningful semantic representation of events starting from a low-level statistical analysis of data gathered form sensing devices. The acquired knowledge is exposed to the outside world like in a blog and progressively enriched during the objects lifetime. The presented paradigm ideally applies to cyber-physical systems, where several mobile heterogeneous micro-devices cooperate to connote and modify appropriately the environment they are dipped in.
|Titolo:||Object (B)logging: Semantic Self-Description for Cyber-Physical Systems|
|Data di pubblicazione:||2017|
|Nome del convegno:||International Joint Conference on Rules and Reasoning, RuleML+RR 2017|
|Appare nelle tipologie:||4.3 Poster|