A knowledge graph is the smallest unit of information that LLMs use that makes sense for both humans and bots. Like a sentence in natural language, it arranges data in a meaningful way, showing how data bits like words are related to each other.

Some blockchain protocols now use knowledge graphs to organize facts and put them on-chain. By connecting these graphs, they create a web of context that makes it simple to find accurate information about anything recorded there.

Imagine trying to make sense of a giant network of facts. A knowledge graph is how machines do it. It is basically a way of mapping the world as a web of entities and relationships. Instead of forcing everything into tables like a spreadsheet, it connects real-world things (people, places, concepts) through labeled links.

Everything in a knowledge graph is stored as a triple: (subject) – [relationship] → (object). For example, (Satoshi Nakamoto) – [authored] → (Bitcoin Whitepaper), or (Bitcoin Whitepaper) – [hasTitle] → (“Bitcoin: A Peer-to-Peer Electronic Cash System”). You can also represent (Satoshi Nakamoto) – [mined] → (Block 0) and (Block 0) – [containsMessage] → (“The Times 03/Jan/2009 Chancellor on brink of second bailout for banks”).

Triples can describe relationships between people too: (Hal Finney) – [sentFirstTransactionTo] → (Satoshi Nakamoto) or between projects and concepts: (Ethereum) – [introduced] → (Smart Contract), (Vitalik Buterin) – [proposed] → (ERC-20 token standard), and (Uniswap) – [builtOn] → (Ethereum). Even broader ideas can be captured, like (Web3) – [aimsFor] → (Decentralization) or (Chainlink) – [provides] → (Decentralized Oracles).

Each triple captures a single, precise fact, and when you connect thousands of them, you get a graph network that shows how all these entities relate to each other.

Famous real-world knowledge graphs include Google’s Knowledge Graph, Wikidata, Microsoft Satori, Amazon’s Product Graph, Facebook’s Social Graph, and many others. Companies also build their own enterprise graphs, like Uber’s graph of drivers, riders, and cities or Airbnb’s graph of listings, hosts, and guests.

Knowledge graphs are designed to be machine-readable. An AI can follow the links to answer complex questions step by step. For instance, if you ask, “Which layer-2 projects were founded by Ethereum core contributors?” the AI can trace paths in the graph: Ethereum → core contributor → person → founded → Arbitrum/Optimism/etc. It’s like giving the AI a map and a compass for reasoning.

These graphs are also dynamic. Real-time data feeds (on-chain metrics, blockchain events, or updated token info) can refresh the graph constantly, so AI models can stay accurate without being retrained from scratch. And when a model generates an answer, it can check each fact against the graph, ensuring it doesn’t hallucinate or get details wrong. Of course, the models we know today are not completely accurate, but they are improving.

Today, December 4th, Fusaka is trending. If you don’t know what it is, you can ask your favorite large language model to explain it. It will do so by relying on knowledge graphs, like these triplets: (Fusaka) – [isUpgradeOf] → (Ethereum) and (Fusaka) – [activatedOn] → (2025-12-03).

Each triple captures a single fact in an unambiguous way, like (Fusaka) – [introduces] → (PeerDAS) or (PeerDAS) – [reducesBandwidthFor] → (Validators).

The graph can also show implementation relationships, for example, (Fusaka) – [implements] → (EIP-7918) and (EIP-7918) – [ensures] → (Consistent ETH Burn). Triples can be linked together to describe broader structures, such as (Fusaka) – [enablesScalingFor] → (Layer 2 Networks) and (Layer 2 Networks) – [targetTPSPostFusaka] → (100000+). Even preparations for future events can be captured, as with (Fusaka) – [preparesFor] → (BPO Hard Forks).

Of course, this is just one aspect of what LLMs do. They’ll take this knowledge and turn it into full text, following your prompt. The style will differ depending on whether you want a simple explanation for yourself or you’re a journalist who suddenly has to pretend to know what Fusaka is and want a wordy, authoritatively-sounding article.

As for myself, I don’t fully understand it either, and I don’t aim to pretend. I’ve just asked Grok for these triplets, as I thought they would be a good illustration of how things work.

In essence, no matter what your prompt looks like and what you want to do with the output, the underlying knowledge graphs will be the same.

But to be useful, knowledge graphs need to be fully accurate, with traceable sources. That’s something LLMs struggle with, because when key info is missing, they just make it up. That’s true for most of them, most of the time. You simply can’t trust AI to curate truth in the form of knowledge graphs.

This is the problem AI leaves open, but some blockchain protocols aim to close it.

Typically, blockchains don’t “think” or run knowledge graphs inside the protocol. They only write raw data into blocks. But some protocols now run knowledge graphs inside the chain. They store structured facts as part of the protocol itself and treat entities and relationships as first-class data.

The Graph protocol ($GRT) is like the Google of blockchains. But, instead of indexing websites, it organizes everything happening on Ethereum, Solana, Polygon, and dozens of other chains into a living map of who did what and when. These records link together into a shared knowledge graph. Overall, it makes web3 more trustworthy.

Geobrowser is a web3 browser and knowledge‑graph app built on top of The Graph. It aims to turn blockchain data (and any other public data) into a browsable, verifiable, decentralized map of knowledge. It is organized around Spaces, hubs where people collect facts, link data, and build a shared knowledge graph. Facts are stored as entities and relationships, the core building blocks of a graph. Most importantly, it turns blockchain data into a shared, user-friendly knowledge base. It gives web3 a real possibility to store and share meaning rather than plain data.

OriginTrail ($TRAC) runs a shared multichain knowledge graph. It stores “Knowledge Assets,” which link real-world data to on-chain proofs. It works across Polkadot, Ethereum, and other chains. It’s used for supply chains, product traceability, and AI-ready datasets.

A few other systems follow similar ideas, store structured facts on-chain, and let apps query them without trusting a middleman.

The bottom line is that blockchain gives you trust, and the knowledge graph gives you structure. Together they form a new data layer for AI, dApps, and real-world systems.

My first contact with knowledge graphs was through my work on a pioneering LLM-training project years ago. One of my frequent tasks was to take a group of triplets (we didn’t call them knowledge graphs, but essentially, that’s what they were) and write meaningful paragraphs using the information provided. Later, I worked on a plethora of different linguistic tasks, but this one was one of the first, and perhaps the most important ones.

Later, my interests shifted toward web3. I’ve been excited about the potential of a decentralized internet for a long time. I see it as a way to return control to users, but also as a way to transform how knowledge itself is structured and accessed. Imagine a web where every fact, every connection, every insight is part of a living network. Instead of being scattered across opaque silos, everything is linked, traceable, and transparent. A system that doesn’t just store data, but organizes it into knowledge humans can actually use.

Knowledge graphs are the blueprint for this vision. They reveal relationships, context, and provenance. In a decentralized web, a knowledge graph would let us trace how ideas evolve, see the sources behind claims, and understand how different pieces of information connect. That’s the future I’m looking forward to.