Semantic Data Lakes: Integrating Big Data and Knowledge Graphs for Enterprise Decision Support

Abstract

The exponential growth of heterogeneous enterprise data has exposed the limitations of traditional Big Data architectures, which prioritize storage scalability over semantic understanding. This paper presents a Semantic Data Lake (SDL) framework that integrates Big Data technologies with knowledge graphs and ontology-driven reasoning to enhance enterprise decision support. The SDL architecture introduces a multi-layered system encompassing data ingestion, semantic annotation, ontology alignment, and graph-based reasoning for contextual query processing. Implemented using Hadoop, Spark, and GraphDB, the framework demonstrates superior performance in query efficiency, scalability, and semantic accuracy compared to conventional data lakes. Experimental evaluations show up to a 40% reduction in query latency and a 19% improvement in semantic precision, achieved through ontology mapping and reasoning-based query optimization. The results validate that semantic enrichment transforms data lakes into intelligent ecosystems capable of delivering explainable, context-aware analytics. The paper concludes by outlining future research directions, including AI-driven ontology learning, federated semantic integration, and hybrid reasoning for real-time knowledge discovery.