  Significant efforts have been made to understand and document knowledge
related to scientific measurements. Many of those efforts resulted in one or
more high-quality ontologies that describe some aspects of scientific
measurements, but not in a comprehensive and coherently integrated manner. For
instance, we note that many of these high-quality ontologies are not properly
aligned, and more challenging, that they have different and often conflicting
concepts and approaches for encoding knowledge about empirical measurements. As
a result of this lack of an integrated view, it is often challenging for
scientists to determine whether any two scientific measurements were taken in
semantically compatible manners, thus making it difficult to decide whether
measurements should be analyzed in combination or not. In this paper, we
present the Human-Aware Sensor Network Ontology that is a comprehensive
alignment and integration of a sensing infrastructure ontology and a provenance
ontology. HASNetO has been under development for more than one year, and has
been reviewed, shared and used by multiple scientific communities. The ontology
has been in use to support the data management of a number of large-scale
ecological monitoring activities (observations) and empirical experiments.

Artificial Intelligence;Computers and Society