Use Data Or Be Used By Data!

The May 26 issue of Seotistics is here for you!

Knowledge graphs have been making their way through in the last few years.

Small and medium companies have no clue but corporations are currently employing such technologies!

So why am I even mentioning them?

And what have these strange things to do with AI?

P.S. This topic is huge, expect a more proper coverage in either my website or course.

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The Google Flashback

In the far May 16 2012, Google mentioned to have a Knowledge Graph.

Back then, it didn’t really matter for practitioners.

Some concepts were introduced here and there and tools like InLinks and WordLift managed to conquer some market share in the SEO world.

We are not here to talk about one channel, though.

What The Heck is a Knowledge Graph (KG)

A knowledge graphs is a type of graph, which is a set of nodes and edges.

Nodes = entities or concepts, e.g. Web Analytics

Edges = relationships, aka connections between nodes.

But the entire idea revolves around the concept of an RDF triple (subject, predicate, object).

E.g. Paul (SBJ) eats (PRD) an apple (OBJ).

In this case, there is a directed edge, Paul is pointing to Apple.

This is a powerful concept because you can classify the concepts that belong to a given domain in a company.

You can say KGs describe entities and their relationships.

If you strip the data out a KG, you get an ontology.

An ontology doesn't go into detail but it defines how these rels are handled and which classes you have in a domain.

What does it even mean?

It means that you can explain univocally what is what and how the business works.

Let’s consider the following example:

Being technology-agnostic is fine but doable.

Now, the cool part about this is about the properties the graphs have:

Connecting different domains aka bypassing silos, e.g. product to sales
Contextual Analysis
Inference

Customer –purchased–> Product –similar_to–> Other_Products | | └–influenced_by–> Campaign –targets–> Demographics | └–uses–> Channels –reaches–> Audience

You can also use a language like SPARQL to extract data from a KG.

Imagine SQL but for RDF triples, this is the gist!

I studied a little bit of it and was super confused by some concepts back then… now it’s much more clear.

Oh, and did I mention we have LLMs to help us too?

Taxonomies

These are used to rank and classify metrics, for example.

Say you want to have your users metrics together, how would you classify them?

For large projects, these are a must because they allow you to define metrics and have a clear understanding of what is what.

Also, in big organizations there is no alignment between departments, it’s like a set of micro-states fighting each other.

A sample taxonomy that is classifying product categories in the Ecommerce category inside Web data.

On a recent project of mine, I had to create a taxonomy for some customer metrics and unify their definitions.

It's extremely invaluable to have clarity and an idea of what you should consider for the business.

The Intersection With Analytics

We are used to tables and “2D” analysis.

Say you want to understand how many people from the USA who bought from you given a campaign happened last month coming from social media.

If you have some experience with tools, this shouldn’t be super hard to pull off.

But what I asked you to also tell me how many bought products with a specific property, like being compliant to a regulation?

Cross-domain analysis is where KGs excel… as they also preserve the context around your entities.

If you may, you can also go crazy with websites:

scrape website
build a KG after it
analyze its main topics

Which is exactly one of the things where ContentMaps excel.

The Intersection With AI

Guess who else loves structured knowledge?

Machines (LLMs)!

Remember that LLMs can still hallucinate though and that’s part of the game.

Article content — From Hank's article on "Navigating Large Language Model Reliability For Leaders".

Knowledge Graphs provide dynamic knowledge and some additional context to LLMs.

This is one of the reasons why Knowledge Graphs are popular again.

In some of my previous issues I mentioned RAG,

Many of these use cases are still too overkill for most of us, that’s why I barely mention it.

How Is It Relevant For Me?

If you don’t work on huge projects this is still relevant for you on a much smaller scale.

Documenting what you do and creating your personal knowledge base is what I’ve been preaching in the last Seotistics issues.

You can represent KG via Mermaid, a diagramming language that is quite easy to learn and reproduce with Claude.

These are nice mockups you can show your stakeholders to explain concepts.

Productivity-wise, my MVP tool Obsidian automatically creates a graph based on how you connect your notes.

This is crazy useful for retrieving pages and understand relationships between topics (plus it looks cool).

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Marco Giordano
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🧠 Knowledge (Graphs) In The AI Era