What is a knowledge graph?
A knowledge graph (KG) is a structured representation of information that organizes entities (such as datasets, publications, or energy assets) and the relationships between them. Graph nodes represent entities and the edges between represent the relations between these entities. Instead of storing data in isolated silos, knowledge graphs connect information in a semantic network, making links between different data sources explicit and machine-readable. They typically build on semantic web technologies such as RDF, OWL, and SPARQL, and can integrate data from diverse origins in a way that supports both human understanding and computational reasoning.
Why do knowledge graphs matter?
Knowledge graphs are critical for enabling integration, interoperability, and discoverability of heterogeneous research data. They are able to depict complex relations between entities without relying on pre-set schemata and thus allow for the flexible integration of different data sources. In the energy domain, data originates from experiments, simulations, sensor measurements, and publications. By representing them in a knowledge graph, it becomes possible to discover connections across projects and disciplines, ensure compliance with the FAIR principles (Findable, Accessible, Interoperable, Reusable), support semantic search and intelligent data services, and track provenance and context, enhancing trust in research results.
What kinds exist?
Knowledge graphs can vary widely depending on their scope and use case one can differentiate between domain-specific KGs (e.g., energy system models, material science data), cross-domain research KGs (e.g., Open Research Knowledge Graph), and enterprise KGs (used in companies for data integration and decision support).
How to select and use one?
The choice for the right knowledge graph depends on domain coverage (energy-specific vs. general scientific), standards support (use of established ontologies, FAIR compliance), and integration needs (ability to link to external sources).
In practice, selection often involves extending or linking to existing KGs, and using them as a backbone for metadata services and semantic search interfaces.
Relevance in the NFDI4Energy Context
Knowledge graphs are central for NFDI4Energy because they provide the infrastructure for linking and enriching energy research data. They enable seamless integration of heterogeneous datasets from different projects and institutions and serve as the foundation for metadata services and semantic search.