Explainer · Plain-language

Research Knowledge Graph: Definition, Meaning & Examples | CASRAI

A research knowledge graph is a structured network that connects the entities of the research ecosystem — people, organisations, outputs, grants, and projects — using persistent identifiers and open metadata. Representing scholarship as linked relationships rather than isolated records makes it possible to trace how research is connected, funded, and reused.

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The step most authors miss

Doing CRediT right? Don’t stop at the statement.

A CRediT statement credits you inside one paper. The recognition CRediT was built for happens when those roles are tied to you, persistently. Sign in with your ORCID — free — and claim your CRediT contributions on casrai.org, the home of the standard. They become a verified, portable part of your identity, not a line that disappears into one PDF.

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Entities, relationships and PIDs

A knowledge graph represents information as entities (nodes) and the relationships (edges) between them. In a research knowledge graph the entities are the building blocks of scholarship — researchers, organisations, publications, datasets, software, grants, and projects — and the edges capture how they relate: who authored what, which grant funded which output, what an article cites. Persistent identifiers make this reliable: ORCID iDs identify people, ROR IDs identify organisations, and DOIs identify outputs, datasets, and increasingly grants, so connections resolve to the right entity rather than an ambiguous name string.

Why connect the metadata

When scholarly records sit in separate silos, questions such as "what has this group published from this grant, and how was it reused?" are hard to answer. A knowledge graph links the records so those relationships become queryable. This supports discovery, research-information management, funder reporting, impact tracing, and analytics — and it depends on open, well-structured metadata being contributed by the community infrastructures that mint and register identifiers.

Real-world research knowledge graphs

The DataCite PID Graph connects persistent identifiers and their metadata — linking, for example, researchers to their datasets, publications, and organisations. OpenAlex (from the non-profit OurResearch) is a free, open index of scholarly works, authors, institutions, sources, and concepts, exposed as a connected graph. The OpenAIRE Graph aggregates metadata about publications, data, software, projects, and funding across Europe and beyond. Together these illustrate the open-infrastructure approach to building research knowledge graphs.

Open metadata as the foundation

A knowledge graph is only as good as the metadata feeding it. Initiatives such as the Initiative for Open Citations (I4OC) and the Initiative for Open Abstracts (I4OA), together with the move by Crossref and DataCite to expose rich, openly licensed metadata, make it possible to build and reuse research knowledge graphs without proprietary lock-in. Contributing complete, identifier-rich metadata at the point of registration is what keeps the graph connected.

Key facts

At a glance

Definition: Linked network of research entities and relationships
Entities: People, organisations, outputs, datasets, grants, projects
Anchored by: PIDs — ORCID (people), ROR (orgs), DOIs (outputs/grants)
Examples: DataCite PID Graph, OpenAlex, OpenAIRE Graph
Depends on: Open, well-structured metadata from community infrastructure
Enables: Discovery, reporting, impact tracing, analytics

Common misconceptions

What people often get wrong

Often heard: A knowledge graph is just a database of papers.

Actually: No — its value is in the typed relationships between entities (authored, funded, cites, affiliated-with), not in storing records in isolation.

Often heard: Research knowledge graphs are proprietary products.

Actually: No — major examples such as the DataCite PID Graph, OpenAlex, and the OpenAIRE Graph are built on open metadata and are openly available.

Often heard: You can build one without persistent identifiers.

Actually: In practice no — without PIDs (ORCID, ROR, DOIs) entities cannot be reliably disambiguated and the connections become ambiguous name strings.

Going deeper