Assessing the Efficiency of Knowledge Representation Models in Libraries: Ontologies, Taxonomies, and Semantic Networks

Abstract

Efficient knowledge representation is crucial for optimizing information retrieval in library systems. This study evaluates and compares Ontologies, Taxonomies, and Semantic Networks based on key performance metrics, including retrieval speed, accuracy, and adaptability. Experimental results indicate that Semantic Networks achieve the fastest retrieval times (180 ms) and the highest scalability (12 million indexed documents), making them ideal for large-scale digital libraries. Ontologies outperform other models in accuracy (92%) and adaptability (9/10), enabling precise semantic reasoning and flexible updates. Taxonomies, while still relevant, exhibit the slowest retrieval speeds (350 ms) and limited scalability (5 million documents), making them less suitable for dynamic environments. These findings highlight the strengths and limitations of each model, providing insights into selecting the optimal knowledge representation framework for modern library systems.