RDF In The Modern Data Ecosystem: Semantic Integration And Advanced Query Optimization

Abstract

The exponential growth of heterogeneous data across modern enterprises has created a pressing demand for semantic interoperability and intelligent query optimization mechanisms. Resource Description Framework (RDF) has emerged as a foundational model for representing and linking structured and unstructured data across diverse domains. This research explores the role of RDF in the modern data ecosystem, focusing on semantic integration and query performance enhancement. Using the Linked Movie Database (LinkedMDB) as a representative RDF dataset, the study investigates graph- based relationships, ontology structures, and query optimization strategies that enhance retrieval accuracy and computational efficiency. The dataset—comprising thousands of interlinked triples across entities such as films, actors, producers, and genres—serves as a practical ground for evaluating SPARQL query behavior and optimization methods. Preprocessing involves RDF parsing, graph construction, and triple pattern analysis using Python-based tools such as rdflib and networkx. Performance evaluation is conducted using realistic query scenarios, comparing basic graph traversal with op- timized semantic joins and indexing mechanisms. Results demonstrate that semantic- level indexing and cost-based query optimization significantly reduce response time and improve query efficiency without compromising data accuracy. The findings underscore RDF’s vital role in the integration of distributed knowledge bases and its potential to form the backbone of future semantic-aware data management systems.