Does the Semantic Meaning of Class Names Matter? A Study of the Library of Congress Classification

This study examines whether the semantic meaning of Library of Congress Classification (LCC) class names provides additional insight beyond the system's hierarchical structure for organizing knowledge. Using SBERT, a natural language processing (NLP) model for generating semantic embeddings, we investigate the relationship between the semantic meaning of LCC subclass names and the word usage patterns of the texts assigned to these subclasses. Our results show that although LCC subclass names with similar semantic meanings occasionally correspond to similar word usage patterns, there is no consistent relationship between the two. In contrast, whether two subclasses belong to the same main class reliably predicts the similarity of word usage in the texts assigned to them. While semantic embeddings of LCC subclass names offer intriguing possibilities, our findings indicate that the hierarchical structure of the LCC system remains more robust for knowledge organization.