categoryrelatedness

Categoryrelatedness refers to the degree to which two categories are semantically or conceptually connected. It encompasses both taxonomic relatedness, such as one category being a subcategory or sibling of another in a category hierarchy, and thematic or associative relatedness, such as shared contexts, functions, or co-occurring attributes. Researchers study category relatedness to understand how knowledge is organized and retrieved, how categories influence perception and learning, and how foundational categories support reasoning and communication.

In cognitive science and information science, relatedness is quantified in several ways. In taxonomies and ontologies, distance measures such as path length, information-content-based metrics (e.g., Resnik, Lin, Jiang-Conrath) measure how closely related two categories are within a structured graph. In distributional semantics and neural embeddings, relatedness is inferred from co-occurrence or context similarity, so two categories may be semantically related even without direct feature overlap.

Relatedness is distinct from similarity. Similarity usually emphasizes shared features or attributes, while relatedness also captures functional, causal, or contextual connections that may not involve feature overlap. Context and domain influence perceptions of relatedness; two categories may be closely related in one domain but not in another.

Applications include cognitive psychology, information retrieval, and recommender systems. In cognitive psychology, relatedness affects generalization, priming, and transfer of learning. In information retrieval, taxonomies and semantic networks use relatedness to improve search results and query expansion. In recommender systems, category relatedness guides item suggestions. In knowledge graphs and natural language processing, measuring relatedness supports clustering, categorization, and semantic reasoning.

Challenges include polysemy and cross-cultural variation; evaluating relatedness is context-dependent; distinguishing relatedness from similarity can be difficult; measuring it across heterogeneous data sources may be costly.