Human-Aware Sensor Network Ontology: Semantic Support for Empirical Data Collection

TL;DR

This paper introduces the Human-Aware Sensor Network Ontology (HASNetO), which integrates existing ontologies to improve semantic understanding and compatibility of scientific measurements across various ecological and empirical data collection efforts.

Contribution

The paper presents a comprehensive, aligned ontology that unifies sensing infrastructure and provenance concepts, facilitating better data integration and analysis in scientific research.

Findings

Supports data management in ecological monitoring

Enables semantic compatibility assessment of measurements

Used by multiple scientific communities

Abstract

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.