Knowledge Graphs and Semantic Data Integration

This chapter examines knowledge graphs as the semantic integration backbone of next-generation research infrastructures. It traces the theoretical distinctions separating knowledge graphs from ontologies and relational schemas, surveys RDF-based storage and federated query architectures, and reviews ontology alignment, embedding-based representation learning, and knowledge graph completion as mechanisms for reconciling heterogeneous data sources. Applications across biomedicine, cultural heritage, bibliometrics, and recommendation are examined alongside the convergence of knowledge graphs with large language models for explainable, grounded reasoning. The chapter concludes by identifying scalability, governance, and provenance challenges facing research infrastructure architects designing federated, cognitively grounded data systems.

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