A review of ontology-driven big data analytics in healthcare: challenges, tools, and applications

Abstract

Exponential growth in heterogeneous healthcare data arising from electronic health records (EHRs), medical imaging, wearable sensors, and biomedical research has accelerated the adoption of data lakes and centralized architectures capable of handling the volume, variety, and velocity of big data for advanced analytics. However, healthcare analytics faces key challenges such as heterogeneity, where different standards like ICD and SNOMED CT do not align, poor data quality due to missing or noisy EHR and sensor data, privacy risks in sensitive patient records, and scalability issues in real-time ICU and IoT streams. Additionally, semantic inconsistencies, such as recording the same condition differently across systems, hinder interoperability and accurate analysis. Without effective governance, these repositories risk devolving into disorganized data swamps. Ontology-driven semantic data management offers a robust solution by linking metadata to healthcare knowledge graphs, thereby enhancing semantic interoperability, improving data discoverability, and enabling expressive, domain-aware access. This review adopts a systematic research strategy, formulating key research questions and conducting a structured literature search across major academic databases, with selected studies analyzed and classified into six categories of ontology-driven healthcare analytics: (i) ontology-driven integration frameworks, (ii) semantic modeling for metadata enrichment, (iii) ontology-based data access (OBDA), (iv) basic semantic data management, (v) ontology-based reasoning for decision support, and (vi) semantic annotation for unstructured data. We further examine the integration of ontology technologies with big data frameworks such as Hadoop, Spark, and Kafka, highlighting their combined potential to deliver scalable and intelligent healthcare analytics. The findings demonstrate that ontology-driven approaches significantly improve semantic interoperability by aligning heterogeneous standards, enhance data integration and query efficiency across distributed datasets, and enable scalable real-time analytics for continuous patient monitoring. Furthermore, ontology-based reasoning strengthens clinical decision support through context-aware knowledge inference, while key research gaps in large-scale deployment, reasoning complexity, and privacy-aware semantic modeling are identified to guide future research and practical implementation. For each category, recent techniques, representative case studies, technical and organizational challenges, and emerging trends such as artificial intelligence, machine learning, the Internet of Things (IoT), and real-time analytics are reviewed to guide the development of sustainable, interoperable, and high-performance healthcare data ecosystems.