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Knowledge-Driven Activity Recognition and Segmentation Using Context Connections
[chapter]
2014
Lecture Notes in Computer Science
We propose a knowledge-driven activity recognition and segmentation framework introducing the notion of context connections. Given an RDF dataset of primitive observations, our aim is to identify, link and classify meaningful contexts that signify the presence of complex activities, coupling background knowledge pertinent to generic contextual dependencies among activities. To this end, we use the Situation concept of the DOLCE+DnS Ultralite (DUL) ontology to formally capture the context of
doi:10.1007/978-3-319-11915-1_17
fatcat:suke2z6mbfehrhcch3qvtryzme