Literature Review for Accuracy Calculation

Accuracy calculation is a crucial factor in evaluating query and data retrieval systems across multiple paradigms, including keyword-based and semantic-based for relational database, and graph database approaches. Keyword-based systems depend on exact or partial string matches, which often fail to capture deeper contextual meaning. Semantic-based methods leverage techniques like cosine similarity to analyze meaning and improve accuracy in retrieving relevant results. In relational data-bases, accuracy calculation typically involves matching strict conditions through structured queries, which perform well with normalized data but falter in modeling complex relationships. In contrast, graph databases, such as Neo4j, utilize graph structures and semantic queries, offering improved accu-racy in capturing relationships between entities. Previous research demonstrates that semantic-based methods consistently outperform keyword-based approaches in complex domains. Graph databases further extend this by representing connections explicitly. However, challenges remain, such as deter-mining similarity thresholds, handling data sparsity, and ambiguity in user queries. Understanding the strengths and limitations of each approach is critical for developing effective information retrieval systems. This review provides insights into the evolving landscape of accuracy calculation methods.