Human Activity Recognition Models in Ontology Networks. (arXiv:2105.02264v1 [cs.AI])

We present Arianna+, a framework to design networks of ontologies for
representing knowledge enabling smart homes to perform human activity
recognition online. In the network, nodes are ontologies allowing for various
data contextualisation, while edges are general-purpose computational
procedures elaborating data. Arianna+ provides a flexible interface between the
inputs and outputs of procedures and statements, which are atomic
representations of ontological knowledge. Arianna+ schedules procedures on the
basis of events by employing logic-based reasoning, i.e., by checking the
classification of certain statements in the ontologies. Each procedure involves
input and output statements that are differently contextualised in the
ontologies based on specific prior knowledge. Arianna+ allows to design
networks that encode data within multiple contexts and, as a reference
scenario, we present a modular network based on a spatial context shared among
all activities and a temporal context specialised for each activity to be
recognised. In the paper, we argue that a network of small ontologies is more
intelligible and has a reduced computational load than a single ontology
encoding the same knowledge. Arianna+ integrates in the same architecture
heterogeneous data processing techniques, which may be better suited to
different contexts. Thus, we do not propose a new algorithmic approach to
activity recognition, instead, we focus on the architectural aspects for
accommodating logic-based and data-driven activity models in a context-oriented
way. Also, we discuss how to leverage data contextualisation and reasoning for
activity recognition, and to support an iterative development process driven by
domain experts.



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