A Review of Ontology-Driven Big Data Analytics in Healthcare: Challenges, Tools, and Applications

2025-10-07

AI Summary

Heterogeneous healthcare big data—including electronic health records (EHRs), medical imaging, wearable sensor streams, and biomedical omics data—frequently devolve into “data swamps” due to weak semantic interoperability, poor discoverability, and insufficient domain-aware access mechanisms. Method: This paper conducts a systematic literature review and proposes a six-category ontology-driven healthcare analytics taxonomy. Methodologically, it integrates ontology modeling, knowledge graph construction, semantic reasoning, and Ontology-Based Data Access (OBDA) within scalable big data infrastructures—including Hadoop, Spark, and Kafka. Contribution/Results: It is the first study to systematically identify emerging trends in semantic interoperability enhancement and synergistic knowledge graph–AI analytics. It synthesizes key technical challenges and pragmatic implementation pathways driven by IoT integration and real-time analytics. Furthermore, it establishes a theoretical framework and an integrated architectural paradigm for building sustainable, interpretable, and scalable healthcare data ecosystems.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Ontologies
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless 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, 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, Kafka, and so on, highlighting their combined potential to deliver scalable and intelligent healthcare analytics. 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.

Problem

• Managing exponential growth of heterogeneous healthcare data
• Addressing semantic interoperability challenges in healthcare analytics
• Integrating ontology technologies with Big Data frameworks

Innovation

• Ontology-driven semantic data management for healthcare
• Integration of ontologies with Big Data frameworks
• Semantic modeling and reasoning for decision support