Find out more about this concept and how can you get involved
What is a Knowledge Graph?
A Knowledge Graph isn’t a fixed concept. It sits at the intersection of technical models, semantic strategies, and real-world applications. The term spans multiple domains — from AI and data integration to information systems — and refers to architectures ranging from ontologies to property graphs.
At its core, a graph is a mathematical structure: nodes (things) connected by edges (relationships). Graphs show how things are linked, but not what those things mean. Without context, a graph is structure only — not knowledge. A metro map is a graph; it shows stations and routes. A GPS system that knows place names, route types, and context? That’s a Knowledge Graph. A Knowledge Graph adds two key layers: Identity — each node represents a specific, identifiable entity. Semantics — each connection carries meaning and context. With these, a graph can support reasoning, integration, and explainability.
Graphs reveal structure. Semantics reveal meaning. Raw data — unstructured text, scattered metadata, or isolated datasets — is fragmented and ambiguous. Graphs organize it, but semantics make it interoperable, interpretable, and reusable. They tell us what each node is, how each link relates, and why it matters. When semantics are added, a simple graph becomes a Knowledge Graph — a powerful framework for connecting, understanding, and reasoning over information.
How is a Knowledge Graph built?
A graph database is exactly what it sounds like — a database that stores nodes and edges. It’s excellent for exploring connections and running complex queries. But that doesn’t automatically make your data a Knowledge Graph. A Knowledge Graph isn’t just about storage — it’s about meaning. It needs a schema, semantics, and a shared understanding of what each node represents, not just how it connects. You can have a Knowledge Graph without a graph database. Some exist in relational databases, others in RDF triple stores, or even in flat files exposed through APIs. And the reverse is true as well — you can have a graph database without a real Knowledge Graph.
During the 2025 Innovation Prototyping Lab, Julien Homo (FoxCub) provided an overview of what a Knowledge Graph is and how it can be built as well as made tangible. You can watch the linked video on the left.
Why are we building the GRAPHIA Knowledge Graph?
SSH knowledge lives in many forms, archives, surveys, scientific instruments, publications, heritage sites, geospatial records, oral histories, and more. Each dataset tells part of the story, but rarely connects to the others.
This creates real challenges:
We can’t easily trace what we know about a person, a place, or an event
We lose links between evidence, sources, and interpretation
We struggle to reuse knowledge across disciplines and tools
GRAPHIA brings these pieces together into a shared Knowledge Graph so we can:
Connect records that belong together (even when they come from different institutions)
Give context to observations (how, when, and why a piece of knowledge was produced)
Explore the relationships that shape social and cultural phenomena
Verify and enrich information with trusted references and expert feedback
Open access to data that was technically findable but practically unreachable
It’s about removing barriers that currently slow down SSH research: silos, incompatible formats, missing links, and unclear provenance. GRAPHIA doesn’t replace existing resources, it connects them, so knowledge becomes easier to discover, understand, and reuse.
Julien Homo, FoxCub
In this video you can watch a walk through some concrete use cases for the GRAPHIA Knowledge Graph in SSH.
How can you get involved in building the GRAPHIA Knowledge Graph?
Do you have an interesting viewpoint or use case to share? Contact us at [email protected] to start the conversation. You can also subscribe to our newsletter or follow our events page.
Contact
Contact our GRAPHIA communication team via the email address:
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