The year of the Knowledge Graph (2025)
There are a lot of signals converging on this being the year of the Knowledge Graph.
Before we get too carried away with this prognosis, let’s review some of the previous candidates for year of the Knowledge Graph, and see why they didn’t work out.
2001
Clearly the first year of the Knowledge Graph was 2001, marked by the unveiling of the Semantic Web by Tim Berners-Lee, James Hendler and Ora Lassila in Scientific American1. This seemed like it was the year of the Knowledge Graph (even though the term “Knowledge Graph” wouldn’t come into widespread use for over a decade). They were talking about the same technology, even the exact same standards.
What made it especially seem like it was the year of the Knowledge graph was that it was only ten years earlier that Tim Berners-Lee had unleased the World Wide Web, and it seemed like lightning was going to strike twice. It didn’t. Not much happened publicly for the next decade. Many companies were toiling in stealth, but there were no real breakthroughs.
2010
Another breakthrough year was 2010, with the launching of DBPedia as the hub of the Linked Open Data movement. DBPedia came out of the Free University of Berlin, where they had discovered that the info boxes in Wikipedia could be scraped and turned into triples with very little extra work. By this point the infrastructure had caught up to the dream a bit, there were several commercial triple stores, including Virtuoso which hosted DBPedia.
The Linked Open Data movement grew to thousands of RDF linked datasets, many of them publicly available. But still it failed to reach escape velocity.
2012
Another good candidate is 2012 with the launch of the Google Knowledge Graph. Google purchased what was essentially a Linked Open Data reseller (MetaWeb) and morphed it into what they called the Google Knowledge Graph, inventing and cementing the name at the same time. Starting in 2012 Google began the shift from providing you with pages on the web where you might find the answers to your questions, to directly answering them from their graph.
Microsoft followed suit almost immediately picking up a Metaweb competitor, Powerset, and using it as the underpinning of Bing.
Around this same time, June of 2009 Siri was unveiled at our Semantic Technology Conference. This was about a year before Apple acquired Siri, Inc., the RDF based spin off from SRI international and morphed it into their digital assistant of the same name.
By the late 20teens, most of the digital native firms were graph based. Facebook is a graph, and in the early days had an API where you could download RDF. Cambridge Analytics abused that feature, and it got shut down, but Facebook remains fundamentally a graph. LinkedIn adopted an RDF graph and morphed it to their own specific needs (two hop and three hop optimizations) in what they call “Liquid.” AirBnB relaunched in 2019 on the back of a Knowledge Graph to become an end-to-end travel platform. Netflix calls their Knowledge Graph StudioEdge.
One would think about Google’s publicity and the fact that they were managing hundreds of billions of triples, and with virtually all the digital natives on board, the enterprises would soon follow. But they weren’t. A few did to be sure, but most did not.
2025
I’ve been around long enough to know that it’s easy to get worked up every year thinking that this might be the big year, but there are a lot of dominos lining up to suggest that we might finally be arriving. Let’s go through a few (and let me know if I’ve missed any).
It was tempting to think that enterprises might follow the FAANG lead (Facebook, Amazon, Apple, Netflix, and Google) as they have done with some other technologies, but in this case they have not yet followed. Nevertheless, some intermediaries, those that tend to influence Enterprises more directly seem to be on the bandwagon now.
Service Now
A few years ago, Service Now rebranded their annual event as “Knowledge 202x2” and this year acquired Moveworks and Data.World. Gaurav Rewari, an SVP and GM said at the time: “As I like to say, this path to agentic ‘AI heaven’ goes through some form of data hell, and that’s the grim reality.”
SAP
As SAP correctly pointed out in the October 2024 announcement3 of the SAP Knowledge Graph As they said in the announcement, “The concept of a knowledge graph is not new…” Earlier versions of HANA supported openCyber as their query language, the 2025 version brings RDF and OWL to the forefront, and therefore top of mind for many enterprise customers.
Samsung
Samsung recently acquired the RDF triple store vendor RDFox4. Their new “Now Brief” (a personal assistant which integrates all the apps on your phone via the in-device knowledge
2 https://www.servicenow.com/events/knowledge.html
3 https://ignitesap.com/sap-knowledge-graph/
4 https://news.samsung.com/global/samsung-electronics-announces-acquisition-of-oxford-semantic technologies-uk-based-knowledge-graph-startup
graph) is sure to turn some heads. In parallel this acquisition has launched Samsung’s Enterprise Knowledge Graph project to remake the parent company’s data landscape.
AWS and Amazon
Around 2018 Amazon “acqui-hired” Blazegraph, an open-source RDF graph database, and made it the basis of their Neptune AWS graph (offering the option of RDF graph or Labeled Property Graph, and working on a grand unification of the two graph types under the banner of “OneGraph”).
As significant as offering a graph database as a product, is their own internal “dogfooding.” Every movement of every package that Amazon (the eCommerce side) ships is tracked by the Amazon Inventory Graph.
graphRAG
Last year everyone was into “Prompt Engineering” (no, software developers did not become any more punctual, it was a job for a few months to learn how to set up the right prompts for LLMs). Prompt Engineering gave way to RAG (Retrieval-Augmented Generation) which extended prompting to include additional data that could be used to supplement and LLMs response.
A year in and RAG was still not very good at inhibiting LLMs hallucinatory inclinations. Enter graphRAG. The underlying limitation of RAG is that most of the data that could be queried to supplement a prompt, in the enterprise, is ambiguous. There are just too many sources, too many conflicting versions of the truth. Faced with ambiguity, LLMs hallucinate.
GraphRAG starts from the assumption (only valid in a handful of companies) that there is a grounded set of truth that has been harmonized and curated in the enterprise knowledge graph. If this exists it is the perfect place to supply vetted information to the LLM. If the enterprise knowledge graph doesn’t exist, this is an excellent reason to create one.
CIO Magazine
CIO.com magazine proclaims that Knowledge Graphs are the missing link in Enterprise AI5 To quote from this article: “To gain competitive advantage from gen AI, enterprises need to be able to add their own expertise to off-the-shelf systems. Yet standard enterprise data stores aren’t a good fit to train large language models.”
CIO Magazine has a wide following and is likely to influence many decision makers.
Gartner
Gartner have nudged Knowledge Graph into the “Slope of Enlightenment”6
5 https://www.cio.com/article/3808569/knowledge-graphs-the-missing-link-in-enterprise-ai.html 6 https://www.gartner.com/en/articles/hype-cycle-for-artificial-intelligence
Summary
Those of you who know me know I’m mostly an anti-hype kind of guy. We, at Semantic Arts, don’t benefit from hype, as many software firms do. Indeed, hype generally attracts lower quality competitors and generates noise. These are generally more trouble than they are worth.
But sometimes the evidence is too great. The influencers are in their blocks, and the race is about to begin. And if I were a betting man, I’d say this is going to be the year that a lot of enterprises wake up and say, “we’ve got to have an Enterprise Knowledge Graph (whatever that means).”
