Target Ontology

Target ontology research focuses on aligning and transferring knowledge between different knowledge bases or domains, aiming to improve tasks like relation extraction, object tracking, and cross-domain classification. Current efforts leverage large language models, transformer networks, and graph-based methods to achieve zero-shot or few-shot learning, often incorporating techniques like multi-task learning and domain-agnostic priors to enhance generalization and reduce reliance on labeled data. This work is significant for improving the efficiency and accuracy of various AI applications, particularly in areas with limited annotated data, such as medical terminology normalization and cross-domain sentiment analysis.