A top-level ontology for smart environments
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Recognising human activities is a problem characteristic of a wider class of systems in which algorithms interpret multi-modal sensor data to extract semantically meaningful classifications. Machine learning techniques have demonstrated progress, but the lack of underlying formal semantics impedes the potential for sharing and re-using classifications across systems. We present a top-level ontology model that facilitates the capture of domain knowledge. This model serves as a conceptual backbone when designing ontologies, linking the meaning implicit in elementary information to higher-level information that is of interest to applications. In this way it provides the common semantics for information at different levels of granularity that supports the communication, re-use and sharing of ontologies between systems.
Ye, J, Stevenson, GT & Dobson, SA 2011, 'A top-level ontology for smart environments' Pervasive and Mobile Computing, vol 7, no. 3, pp. 359-378.
Pervasive and Mobile Computing
This is the author's version of this article. Published version copyright © 2011 Elsevier B.V is available from www.sciencedirect.cominfo:eu-repo/semantics/openAccess
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