Heterogeneity Reduction for Data Refining Within Ontology Learning Process
Václav Jirkovský, Ondrej Sebek, Petr Kadera, Nestor Rychtyckyj · 2018
The (semi-)automated integration of new information into a data model is a functionality which is required in cases when input documents are extensive and therefore a manual integration difficult or even impossible. We proposed the solution combining the ontology learning process with information acquisition from the Web (web mining). This approach offers a robust way how to integrate even previously unknown information disregarding target application or domain. The solution deals with facilitating identification of input data among existing concepts or with the definition of a new concept. The proposed solution was experimentally verified on the integration of an excel document containing spare parts and Ford Supply Chain Ontology.