Using sensor data to detect time-constraints in ontology evolution

Abstract

In this paper, we present an architecture for time-constrained ontology evolution comprised of two tools: the J2OIM (JSON to Ontology Instance Mapper), which uses JavaScript Object Notation (JSON) objects to populate an ontology, and TICO (Time Constrained instance-guided Ontology evolution), which analyses streams or batches of instances as they are generated and attempts to identify potential changes to their definitions that may trigger evolutionary processes. These tools help compensate for identified gaps in literature in instance mapping and modular versioning. The case-study for these tools involves a predictive maintenance (PdM) scenario in which near real-time data sensor enriched by contextual data is continuously transformed into ontology individuals that trigger ontology evolution mechanisms. Results show it is possible to use the instance mapping mechanisms in an incremental fashion while assuring no duplicates are generated and the aggregation of similar information from distinct data points into intervals. Furthermore, they show how the ontology evolution processes effectively detect variations in ontology individuals, generating and updating existing concepts and roles.