Deep Level-wise XMLC Framework for Semantic Indexing

The system enables automatic self-labeling of scientific literature through deep learning models that autonomously select keywords from domain ontologies without human intervention. The multi-label learning framework allows the system to automatically process and categorize large volumes of literature, eliminating the need for manual expert curation while maintaining high labeling accuracy through learned semantic representations.

The patent replaces the mechanical manual process of expert labeling with an automated computational system. Deep neural networks and multi-label learning algorithms substitute human experts in the labeling process, using learned features and semantic understanding to automatically assign keywords from domain ontologies to scientific literature, thereby dramatically reducing time consumption while preserving accuracy.

The system performs automatic semantic indexing through multi-label learning frameworks that independently process scientific literature. The deep learning models automatically extract semantic information and assign keywords from domain ontologies without requiring human expert involvement, enabling high-throughput processing while maintaining indexing quality through learned semantic representations and hierarchical label structures.

The patent transforms the indexing process by changing from manual parameter-based labeling to automated learned feature-based labeling. Deep neural networks learn optimal labeling parameters and semantic representations from training data, automatically adjusting weighting and selection criteria to maintain high indexing quality while achieving scalable throughput across large literature collections.

The patent segments the extreme multi-label learning problem into hierarchical levels, organizing thousands of labels into structured hierarchies with parent-child relationships. This segmentation divides the vast label space into manageable subsets at different levels, allowing the model to process labels in a structured manner rather than as a single high-dimensional problem, thereby maintaining performance while controlling complexity through hierarchical decomposition.

The patent introduces hierarchical structure as an additional dimension to organize the label space. Instead of treating all labels as a flat high-dimensional space, the system adds a hierarchical dimension that groups related labels together, enabling the model to exploit structural relationships and reduce the effective dimensionality of the problem while maintaining comprehensive label coverage.

The patent replaces manual feature engineering with automated deep learning-based feature extraction. Neural networks automatically learn relevant semantic features and representations from raw text data, substituting the manual expert process with computational algorithms that automatically identify and extract meaningful features, thereby eliminating time-consuming manual feature engineering while maintaining model interpretability through learned feature hierarchies.

The system performs self-featured engineering through deep learning models that autonomously extract and learn semantic features from scientific literature. The multi-label learning framework automatically identifies relevant features and representations without human intervention, enabling the system to adapt to different domains and ontologies while maintaining interpretability through the structured hierarchical label space and learned feature relationships.