Creating a specific domain ontology for supporting R&D in the science-based sector – The case of biosensors

In science-based and technology-intensive projects, knowledge management challenges require a tentative and cautious review of the technological domains, as well as, venues to monitor and assess the way those domains evolve, emerge, mature, and decline. Ontologies play a crucial role in conceptualizing/formalizing domain knowledge, yet any ontological platform that is constructed for supporting R&D throughout the knowledge creation process, must explicitly address the interplay between exploitation and exploration of knowledge at deep and surface levels. Focusing on the product per se and its downstream and upstream knowledge evolution complex system, ontology engineering adopts herein a process-driven view for capturing a continuously changing environment. The authors present a methodological framework for creating specific domain ontologies by means of a cybernetic infrastructure built on a modification of the Nonaka’s SECI process. This rationale is exemplified on biosensors, a class of devices strongly attached to multidisciplinary basic and applied science, bearing along many levels of input and output knowledge. The proposed ontological representation, expresses and defines a target product as a metamodel. Combined with knowledge about the scientific background of the product, an aspect model at physical concept level is generated from the metamodel and is further converted into a design model. This scheme enables knowledge to be used not only for representation but also for reasoning at functional level. The research logic followed herein does not bring yet another ontology building methodology through a project-management context, but rather contributes to an ontological approach for exploring the diverse knowledge inputs that a product requires through a specific domain-derived and domain-oriented context, which relies on a collaborative model building methodology and a systemic modeling formalism by using 2nd order cybernetics in order to include human intervention.

► We adopt a process-driven view for ontology engineering.
► We construct domain ontologies on a cybernetic infrastructure.
► Ontological representation expresses and defines a target product as a metamodel.
► Thus knowledge can be used for representation and reasoning at functional level.
► This rationale is exemplified on biosensors.