Chapter 21. Knowledge Graphs

The future is already here — it’s just not evenly distributed.

— William Gibson, on NPR’s Talk of the Nation, 1999

The name

The state Part II derived is in production at scale, under an industry name. A knowledge graph is instance data and the ontologies that describe it, held and queried as one graph. The name went mainstream in May 2012, when Google introduced its Knowledge Graph — “things, not strings.” Freebase seeded it; Chapter 23 shows Freebase browsed live in 2008. Google has never published what its graph runs on, but the contents are expressible as triples, and its public API serves them that way. Knowledge graphs are also built on other graph models; the ones this chapter counts are RDF.

The first movers

The practice preceded the name, and the first movers published their reasons as they went.

In the world, dated 2010. For the World Cup the BBC generated 700-plus pages from an RDF triple store — more index pages than the rest of BBC Sport combined. By London 2012 the same architecture kept a page for every athlete, team and discipline, ten thousand of them, a scale its architect called “simply impossible to manage using a static CMS driven publishing stack.”

The BBC Olympics athletes page in July 2012: a banner announcing over 10,000 athletes from more than 200 countries, a live medal table, and featured athletes — pages generated from the triple store

In the world, dated 2011. The Danish comics site Helt Normalt rebuilt its publishing on RDF, SPARQL and XSLT. Its builders told a W3C workshop that the codebase shrank by an order of magnitude against the relational system it replaced, and that its ontologies were reused rather than written — down to a zodiac vocabulary found on the open web, for the daily horoscope strip. The platform was Graphity, LinkedDataHub’s predecessor (disclosure: the author’s, per Chapter 18).

The Helt Normalt front page in 2012: a Wulffmorgenthaler strip of a T. rex on a toilet, arms too short for the toilet paper, dated 8 March 2012, with star ratings, Facebook comments, and a column of recent strips on a bright green layout

In the world, dated 2013. NXP Semiconductors, its product data “scattered and duplicated across numerous applications and databases,” published its exit: an HTTP URI per product, every source converted to RDF, SPARQL underneath. “The Linked Data is the API.” NXP’s pages were served by the same Graphity.

The wave

The wave behind the first movers arrived a decade later, at the top of the market. NASA runs the systems engineering of its Moon program on an RDF graph. Siemens holds 1.2 million products in one. The banks maintain FIBO, a shared financial ontology, in OWL. Gartner dated the wave in 2021: “by 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021” — a figure spanning every graph model, not RDF alone. And what accelerated the wave is the machine reader. A statistical model answering business questions over an enterprise database got 17 of 100 right against raw SQL, and 54 against the same data as an RDF graph. Chapter 22 derives what the graph is doing under the model.

The words went mainstream too. Palantir has sold its platform’s core abstraction as the Ontology since 2018 and put the word in its SEC filing in 2020. Microsoft followed: Power BI’s datasets became semantic models in 2023, and Fabric now ships an ontology of its own — “a shared, machine-understandable vocabulary of your business.” Neither ontology runs on RDF. The point is smaller and telling: ontology was an academic word the industry would not say aloud, and now it is a product name.

The open giants

The largest knowledge graphs are not corporate at all. UniProt, the protein knowledge base, has published its data as RDF since 2008 and holds 232 billion triples behind a public SPARQL endpoint — the largest knowledge graph anyone can query. Wikidata — where part of Freebase settled — serves about eighteen billion more. Google last counted its own graph in 2020: 500 billion facts, private, unqueryable. And the open graphs link to one another: the Linked Open Data cloud maps 1,360 interlinked datasets as of June 2026.

The Linked Open Data cloud diagram, version June 2026: more than a thousand circles, each a published dataset, packed into one dense ball and joined by a line wherever one dataset links to another

The head and the tail

A single curve drops steeply from a high head at the left and flattens into a long tail hugging the axis to the right; the short head is blue, the long tail grey; the axis legends read organizations, largest first, and integration pain

The wave stops partway down the market, and the reason is arithmetic. Integration pain scales with organization size: General Electric ran about seventy-five procurement systems, Merck about four thousand Oracle databases. Chapter 17 priced the bridges between silos at N × M, and every acquisition raises the count. Down the tail the count falls to a handful, the bill falls with it, and the case for a knowledge graph never arrives. Adoption tracks the head. That is the distribution the epigraph names: the future, already here, unevenly.

The unclaimed half

Many have realized RDF’s potential for data integration. Very few have realized its potential for web application architecture. The audit says the same in its own columns: the industry adopted the R-rows and left the S-rows unclaimed. A knowledge graph is Chapter 5’s state without Chapter 4’s architecture — the data half of the derivation, running at scale, waiting for the other half. The two chapters that follow are about the other half.