Improving an Environmental Ontology by Incorporating User-Input

Abstract

Ontologies supporting open and intuitive search in heterogeneous databases have been studied by various research groups for several years. A crucial factor for success in the use of ontologies is the adjustment of the conceptualization represented in the data to the conceptualizations of the individual users. The generation of a high-quality ontology is very cost intensive. The ontological opening up of the data content is straightforward, whereas the conceptualizations of the users are rather difficult to grasp. We present a novel approach for reinforcement ontology learning integrating user-input. The knowledge base consists of expert ontologies modeled beforehand by experts and userinput will be used to create an user ontology. This paper presents the concept of how to incorporate user-input into existing ontologies.