Interactive Ontology

Interactive ontology matching aims to improve the accuracy and efficiency of aligning ontologies by incorporating human expertise into automated processes. Current research focuses on developing interactive systems that leverage active learning techniques, such as adaptive parameter control and ensemble methods, to guide user interaction and optimize the matching process, addressing challenges like class imbalance. These advancements are significant for improving data integration and knowledge-based reasoning across diverse domains, particularly in large-scale applications where fully automated methods fall short. The resulting higher-quality ontology alignments have practical implications for various industries, enhancing data interoperability and facilitating knowledge discovery.