Taxonomyguided

Taxonomyguided refers to approaches that rely on an explicit taxonomy or hierarchical classification system to guide learning, inference, or decision making. In such methods, taxonomic relationships between categories, concepts, or entities are embedded into the model design, loss function, or evaluation framework. The result is predictions or analyses that respect the structured relationships defined by the taxonomy even when data are scarce or noisy.

In machine learning and information retrieval, taxonomyguided techniques use hierarchical label spaces, taxonomic constraints, or graph-based regularization. Common implementations include hierarchical softmax or classifiers that traverse a label tree, structured prediction models that enforce parent-child consistency, and embedding methods that encode taxonomy topology into feature representations. Applications span document and content classification with topic taxonomies, image recognition with class hierarchies, biomedical data analysis using organism or disease taxonomies, ecological trait inference, and product categorization in e-commerce.

Benefits and limitations: Benefits include improved sample efficiency through shared information among related classes, better interpretability, and risk reduction for rare classes by leveraging higher-level categories. Limitations include reliance on the quality and completeness of the taxonomy, potential rigidity if the taxonomy does not align with data, added computational overhead, and sensitivity to taxonomy updates.

Future directions include handling dynamic or multi-view taxonomies, integrating multiple hierarchies, learning taxonomies jointly with data, and developing evaluation metrics that reflect hierarchical accuracy and hierarchical loss.