Topic Taxonomy

Topic taxonomy research focuses on building and improving hierarchical classifications of concepts, primarily to organize and access information more effectively across diverse domains. Current efforts concentrate on automating taxonomy creation and expansion using techniques like large language models (LLMs) and hierarchical clustering, often incorporating both textual and structural information to enhance accuracy and efficiency. These advancements are significant for various applications, including text mining, knowledge graph construction, and improving the performance of machine learning models by aligning predictions with established taxonomies. The ultimate goal is to create more robust, comprehensive, and easily updatable taxonomies that better reflect the complexities of real-world knowledge.