Human-Aware Sensor Network Ontology: Semantic Support for Empirical Data Collection
This addresses the problem for scientists in fields like ecology who struggle to integrate disparate measurement data due to conflicting ontologies, representing an incremental improvement through integration.
The paper tackles the lack of integrated ontologies for scientific measurements by presenting the Human-Aware Sensor Network Ontology, which aligns and integrates sensing infrastructure and provenance ontologies to enable semantic compatibility assessment for combining measurements.
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.