The five-minute version

Five ideas, in the order that makes each one necessary. No jargon before it is earned: you will not meet the words ontology or semantic in this chapter, because you do not need them yet. About fifteen minutes if you read it properly.

1 · A node is just a node

Draw a box. Write Review in it. What have you got?

You have a box with a word in it. You do not have a Review. You have a node that someone labelled “Review”, and that label is a claim by whoever drew the box, not a fact about the world. Nothing in the box tells you whether that Review took two minutes or two weeks, whether a human did it, whether it satisfies a regulator, or whether it is the same kind of thing as the Review your colleague drew in a different box yesterday.

A node connected to nothing is meaningless. Literally, not rhetorically: there is nothing there to be right or wrong about.

This sounds like a philosophical point. It is an engineering one, and it has a concrete consequence: if you try to make the box mean something by putting more words inside it, you are solving the wrong problem. Adding type: "security-review" and duration: "2 weeks" gives you a bigger box with more words in it. Someone else's box will use different words for the same thing, and the same words for a different thing, and you will find out at the boundary.

2 · The same value, differently connected, means different things

Here is the example that makes it concrete, and it is real code rather than a metaphor.

Two variables in a Python program. Both hold the number 8080.

The difference is not in the value. Both scenarios have 8080. The meaning is identical in the developer's head. It is radically different in the graph, and the graph is the thing another system, another team, or an agent has to work from.

Which gives the sentence this whole book is built on:

Everything is a graph; meaning is not declared but discovered through relationships; and confidence in that meaning is proportional to how richly a node is connected to other nodes that provide context.

Note what follows immediately: meaning stops being something you assert and becomes something you can compute. That is the move. Everything else in this book is a consequence of it.

3 · Five teams, five processes, one word

This is the best on-ramp in the material, and it is the one to try on a sceptical colleague.

Five organisations each have a thing they call a Review.

The schema-first instinct is to define a Review. Ask which of these five gets to be the definition. Whichever you pick, four organisations now have to lie about their process or exclude themselves from your system. That is not a modelling problem; it is a political one, and it does not have a technical solution.

The graph does not ask anyone to agree. Each keeps its own node, with its own edges: who performed it, what was produced, how long it took, what it referenced, what it authorised. Then you ask a specific question, and the answer is a computation over the overlap.

• “Did somebody other than the author look at this?” A, B, C and E overlap. D does not.
• “Is there a record a third party could inspect?” A, B, C and E. D does not.
• “Would BaFin accept this?” C, and possibly nobody else. (BaFin is Germany's federal financial regulator.)

Three properties of that answer are worth naming, because each one is something a schema cannot do:

• It is not binary. Compatibility is a degree of overlap, not a yes or a no.
• It is not symmetric. C's review satisfies B's question; B's does not satisfy C's.
• It is purpose-relative. The same two processes are compatible for one question and incompatible for the next. There is no global answer, and asking for one is the mistake.

Nobody had to agree on anything, change their process, or adopt a shared vocabulary, and the system still told you exactly where they overlap and where they do not.

4 · “We cannot confirm Z” is a better answer than a guess

If meaning comes from connectivity, then how well connected something is is a measure of how much weight it will bear. That gives a ladder, and every assertion sits somewhere on it:

So the honest output of such a system is not a score. It is three sentences, we know X, we think Y, we cannot confirm Z, each with a reason you can trace.

And the remedy for low confidence follows from the diagnosis. It is enrichment, not enforcement: you add edges, you do not add validation rules. The graph grows; it does not constrain. That distinction is small to state and hard to hold on to, and it is what separates this from every schema-validation system you have used.

5 · Three of ten pieces of evidence is information

Most systems list what they have. An honest one also lists what it lacks.

If you need ten pieces of evidence to support a conclusion and you hold three, that is not a failure state to be hidden behind a progress bar. It is a fact with three uses: it quantifies your confidence, it tells you precisely which seven things to go and get, and it makes the business case for connecting them, because now the missing dots have names.

Every rendered graph in this book uses that palette, and the third one is the point: an unanswered question is drawn, not omitted. It is the same rule the live Risk Graph Explorer already runs. In the Article 26(5) worked example, the nine question nodes (five of them unanswered) are described in the source brief as “the actual output of the exercise”. That example has its own chapter.

A register has empty cells, which look like nothing. A graph has unanswered question nodes and unevidenced facts, which can be counted, queried and assigned to someone.

That is altitude 1

Five ideas: a node alone means nothing; the same value differently connected means different things; nobody has to agree for the overlap to be computable; confidence is a function of connectivity; and a named absence beats a hidden one.

If you stop here you already have the useful half. If you want rules you can apply to your own graph tomorrow (what to call your edges, why one particular edge name is banned, and how to stop the picture turning into a hairball) that is altitude 2.