Part 51 - AI Buddy ..... How do we plan an entire universe of knowledge?
From individual articles to a living system of knowledge
A knowledge universe does not arise because a website has many articles. It arises when knowledge is organized, connected, maintained, and developed as a whole . We can write a hundred good articles without having a knowledge universe. If the articles are isolated, if the same concepts are explained differently, if important questions are missing, and if the reader cannot find his way between the topics, we have built a large content collection in the first place.
A universe of knowledge is something more.
Here, each article has a task.
Theme pages create an overview.
Thematic clusters gather related knowledge.
Technical terms provide precision.
Cornerstone content establishes the academic foundation.
Internal links build the connections.
And behind all this, we need a plan.
Not a plan that locks knowledge forever.
But a structure that allows you to build, learn, update and expand over time .
That is the difference between producing content and managing knowledge.
Magne & the Friend
Magne
Good morning, buddy.
The buddy
Good morning.
Ready to build an entire universe of knowledge?
Magne
Heh heh.
That sounds modest.
Can't we just start with an article?
The buddy
That's exactly what we did.
Magne
Yes.
And then there was another one.
And one more.
And suddenly we had quite a few.
The buddy
This is often how a universe of knowledge begins.
Not with a big map on the wall.
But with good questions.
We start with the questions
Magne
It has actually been a common thread throughout our entire KI series.
The buddy
Yes.
Just look at the titles.
What is artificial intelligence?
How does AI learn?
Where do the answers come from?
How do we organize knowledge?
How do we build theme pages?
Magne
They are questions.
The buddy
And that's no coincidence.
People rarely come to a knowledge universe because they want to read "content."
They come because they want to understand something .
Magne
So when planning a universe of knowledge, we should not start with:
“How many items should we produce?”
The buddy
No.
Instead, start with:
What do the people we write to need to understand?
From questions to disciplines
Magne
But if we get a hundred questions, surely we'll get a new problem pretty quickly?
The buddy
Yes.
Then we need to start looking for patterns.
Which questions are about the same thing?
What questions are fundamental?
Which ones require prior knowledge?
Which ones represent immersion?
Magne
And then the disciplines start to grow?
The buddy
Exactly.
Imagine we put all our questions out on a large table.
Some are about how AI works.
Others about knowledge.
Someone about content strategy.
Others about e-commerce.
And some about how businesses are going to change.
Magne
Then we can group them.
The buddy
Yes.
And suddenly we begin to see the structure of knowledge.
We need a map before we build further.
Magne
So now we create the table of contents?
The buddy
Among other things.
But a good knowledge map should show more than just the order of the articles.
Magne
What else?
The buddy
For example:
What main themes do we have?
What subthemes are there?
Which articles are cornerstone content?
What technical terms are used?
Which articles belong together in topic clusters?
Where are connections found across disciplines?
And last but not least:
What's missing?
Magne
The map then also becomes a planning tool.
The buddy
Yes.
It shows both the knowledge we have...
and the knowledge we have not yet built.
Not all content is equally important
Magne
But if we have a hundred articles, aren't they all important?
The buddy
They can all be useful.
But they don't necessarily have the same function.
Magne
Explain.
The buddy
Some articles introduce an entire field of study.
Some define key concepts.
Some answer very specific questions.
Some give examples.
Some go in depth.
And some tie several disciplines together.
Magne
So we should know what role an article has?
The buddy
It is very useful.
This makes it easier to understand both where it belongs and what it should link to.
A knowledge universe is not a folder structure
Magne
Can't we just create categories and put the articles in the correct folder?
The buddy
That helps.
But knowledge is more complicated than a filing cabinet.
Magne
Because the topics overlap?
The buddy
Yes.
Think about trust .
It can belong under management.
But trust is also relevant to customer experience.
Digitization.
Artificial intelligence.
Innovation.
And value creation.
Magne
So one article can have connections to multiple parts of the knowledge universe.
The buddy
Exactly.
Therefore, we need both hierarchy and network .
Magne
The hierarchy provides order.
The buddy
And the network shows the connections.
Then come the knowledge gaps
Magne
I like this.
Because when we see the whole map, it must also become easier to discover what we are missing.
The buddy
That is one of the biggest advantages.
Maybe we've written ten articles about generative AI...
but no proper article about training data.
Or many articles about e-commerce...
but nothing that explains product information.
Magne
Then we see a hole.
The buddy
Yes.
And then we can prioritize new content because it is lacking in knowledge .
Not just because we need something new to publish on Tuesday.
What about overlap?
Magne
And I suppose the map can also reveal the opposite?
The buddy
Yes.
Maybe we have three articles that actually answer almost the same question.
Magne
Then maybe we don't need a fourth.
The buddy
Exactly.
Rather, we can improve what we already have.
Merge content.
Clarify the difference between the articles.
Or update the best one.
Magne
That's interesting.
Because then content strategy also becomes the art of knowing when we should not produce new content .
The buddy
That is a very important point.
The knowledge universe must be maintained
Magne
But when the map is finished...
So are we done then?
The buddy
Heh heh.
Knowledge has the annoying property that it evolves.
Magne
Especially in artificial intelligence.
The buddy
Definitely.
New technologies are coming.
Concepts change.
Research is evolving.
Old examples become outdated.
New questions arise.
Magne
So a universe of knowledge can never really be "finished"?
The buddy
Not if it's going to be alive.
It must be managed.
From publishing calendar to knowledge plan
Magne
Many businesses have a content calendar.
We will publish this on Monday.
On Wednesday we will publish something else.
We will have a new article on Friday.
The buddy
A publishing calendar can be helpful.
But it primarily answers:
When should we publish?
A knowledge plan asks other questions.
Magne
Seam?
The buddy
What do we need to explain?
What's missing?
What should be updated?
What should be connected?
What is most important to build first?
Magne
There's actually a pretty big difference.
The buddy
Yes.
One organizes the publication.
The second organizes the knowledge .
Where does AI come in?
Magne
Now we almost have to get to artificial intelligence.
Because this sounds like an area where AI can actually help us.
The buddy
It can.
For example, AI can help us analyze large amounts of existing content.
Magne
And find themes?
The buddy
Yes.
It can help identify possible groupings.
Detect overlap.
Find terms that are used differently.
Suggest related articles.
Discover questions that may not be well enough covered.
And help us create a first draft of a knowledge structure.
Magne
Then AI can build the entire universe for us?
The buddy
No.
Magne
That answer came quickly.
The buddy
Heh heh.
Because someone still has to consider:
What is academically important?
What is correct?
What does the reader need?
Which connections are meaningful?
What should be prioritized?
And what kind of knowledge universe does the business actually want to have?
build?
Magne
So AI can help us with the map.
The buddy
Yes.
But humans still have to decide what landscape the map should describe .
Academic specialization
A knowledge universe is more than a content strategy
Traditional content strategy includes planning, production, publishing, management and maintenance of content.
A knowledge universe builds on many of the same principles, but places particular emphasis on the relationships between the elements of knowledge .
The question is not just which pages the website needs.
We also ask:
What does each page represent?
What concepts does it explain?
What other knowledge elements does it build on?
Where can the reader go next?
And what role does the site play in the larger academic whole?
We then move from document orientation towards a more relational understanding of content.
First step: Define the knowledge area
A universe of knowledge needs a professional demarcation.
A website can, in principle, write about anything, but a useful universe of knowledge should have an understandable identity.
We must therefore define:
What subject areas should we cover?
Who are we building knowledge for?
What questions should we help them understand?
How deep should we go?
This creates the framework for further planning.
Second step: Map existing content
Before we produce more, we should know what we already have.
A content mapping can record, among other things:
article title,
main theme,
subtopic,
key technical terms,
target group,
content type,
publication date,
last updated,
related articles,
and what function the page has in the knowledge structure.
Once this is collected systematically, we can begin to analyze the content as a whole.
Step Three: Build the Taxonomy
A taxonomy helps us classify knowledge.
We can define main themes, subthemes, and other properties that make the content easier to organize.
But the taxonomy should not become so complicated that no one is able to use it.
It should reduce complexity.
Don't produce any more of it.
A good taxonomy is therefore developed based on actual knowledge needs and should be able to be adjusted as the universe grows.
Fourth step: Identify the most important knowledge elements
Next, we should identify which parts of the knowledge are particularly central.
It could be:
basic definitions,
cornerstone items,
theme pages,
key technical terms,
important models,
or questions many other articles build on.
These elements become important hubs in the universe of knowledge.
Fifth step: Map the relationships
Once the content is classified, we can begin to describe the connections.
An article can:
explain a concept,
building on another article,
give an example,
elaborate on a sub-topic,
represent a counterargument,
or connect two disciplines.
This is more precise than simply saying that two pages are "related."
We begin to describe why they are related .
It is an important step from a content collection towards a knowledge network.
Sixth step: Find gaps and overlaps
When the structure becomes visible, we can examine where it is weak.
A knowledge gap can be an important question that lacks an answer.
Overlap can occur when multiple pages attempt to solve the same task without the difference between them being clear.
Both are valuable pieces of information.
Knowledge gaps can show where we should develop new content.
Overlap can show where existing content should be consolidated, reworked, or clarified.
Seventh step: Prioritize
A universe of knowledge cannot be built all at once.
Therefore, we need prioritization.
Basic questions should often be addressed before highly specialized in-depth study.
Cornerstone content can be built before the smallest branches.
Large knowledge gaps may be more important than yet another article in an area that is already well covered.
Prioritization should therefore be based on academic importance and user needs , not just publication frequency.
Eighth step: Manage the knowledge
Publication is not the end of knowledge work.
A living universe of knowledge needs routines to:
update,
correct,
expand,
merge,
restructure,
link,
and when necessary, remove or replace content.
This can be described as content management or content governance .
Someone must be responsible for ensuring that the structure continues to function as the universe grows.
AI can help – but should not own the professional structure
Artificial intelligence makes it possible to analyze larger amounts of content faster than before.
Language models can be used to suggest categorization, identify linguistic similarities, compare documents, summarize content, and suggest possible connections, among other things.
But semantic similarity is not always the same as academic relevance.
Two articles can use similar language without filling the same knowledge need.
And two articles with very different wording can have an important academic connection.
Therefore, AI should be used as an analysis and work tool , while the editorial and professional assessment still requires human responsibility.
From website to knowledge infrastructure
When these principles are combined, the website begins to change character.
We no longer just have:
pages + articles + menus.
We get:
concepts + questions + explanations + themes + relationships + sources + learning paths.
This is what makes the concept of a universe of knowledge interesting.
The website will not just be a place where information is published.
It becomes a structure where knowledge can be discovered, understood, connected and further developed .
From questions to a universe of knowledge
How we build the structure layer by layer
Level | What are we building? | What is the task? |
1. Question | What the reader wants to understand | Provides the basis for the content |
2. Article | A precise and limited answer | Building a knowledge element |
3. Technical terms | A consistent conceptual framework | Make knowledge more precise |
4. Theme cluster | Related articles | Collects different pages of the same topic |
5. Cornerstone content | Basic subject content | Provides a foundation for further learning |
6. Theme page | Overview of the subject area | Helps the reader orient themselves |
7. Internal links | Connections between knowledge | Creates natural learning paths |
8. Universe of Knowledge | The entire coherent structure | Turn the content into a living system of knowledge |
How do we plan the universe of knowledge?
From random publishing to systematic knowledge work
Question | What are we investigating? | What can we do? |
What should we cover? | Subject areas and user needs | Define the universe's academic framework |
What do we already have? | Existing articles and resources | Conduct content mapping |
How does the content fit together? | Themes, concepts and relationships | Building taxonomy and topic clusters |
What is basic? | Content many other pages build on | Identifying cornerstone content |
What's missing? | Unanswered questions and weak areas | Identifying knowledge gaps |
What overlaps? | Pages with the same or unclear function | Merge or clarify content |
What should be built first? | Meaning, needs and dependencies | Prioritize content development |
What needs to be maintained? | Outdated or changed knowledge | Establish routines for management |
Where can AI help? | Large amounts of information and patterns | Using AI as an analysis and work tool |
Who is responsible? | Quality, structure and updating | Establish clear content management |
Technical terms
Key concepts when planning a knowledge universe
English technical term | Explanation |
Knowledge Ecosystem / Knowledge Environment | A cohesive environment of articles, concepts, topics, sources and connections that make knowledge easier to find, understand and develop. |
Knowledge Architecture | The overall structure that describes how knowledge elements are organized and related to each other. |
Content Strategy | Planning, developing, managing and maintaining content so that it supports defined user needs and goals. |
Content Inventory | A systematic registration of existing content that provides an overview of what exists and where it is located. |
Content Audit | A qualitative and quantitative assessment of existing content to identify quality, deficiencies, overlaps and needs for improvement. |
Taxonomy | A classification system that organizes content and concepts into defined categories and groups. |
Ontology | A more explicit description of concepts within a field of knowledge and the types of relationships that exist between them. |
Knowledge Graph | A structure that represents knowledge elements and the connections between them as a network of entities and relationships. |
Knowledge Gap | An important question, concept, or perspective that is not adequately covered in existing knowledge. |
Content Governance | Roles, responsibilities, rules and work processes that ensure that content is developed and maintained in a consistent manner. |
Content Lifecycle | The process from planning and production to publishing, maintenance, updating and eventual phasing out of content. |
Semantic Relationship | A meaningful connection between concepts or elements of knowledge based on what they represent and how they are related. |
We have only just begun.
Magne
Friend...
Now I think we've actually done something pretty important.
The buddy
What are you thinking about?
Magne
We started this section by talking about content strategy.
But now it's almost not about "content" anymore.
The buddy
What is it about then?
Magne
Knowledge.
How we build it.
How we organize it.
How we explain it.
How we connect it together.
And how we ensure it remains useful as the world changes.
The buddy
Then you have understood the whole point of this section.
Magne
But I also see something else.
The buddy
What then?
Magne
We have spent a lot of time building a universe of knowledge.
But in the end, someone has to use the knowledge for something .
The buddy
Yes.
And now an interesting shift is happening.
Magne
From knowledge...
The buddy
...to action.
Magne
We have talked about AI helping people find and understand information.
But what happens when AI starts to meet the customer ?
The buddy
Then we move from content strategy to commerce.
From articles and knowledge structure...
to products, needs, choices and decisions.
Magne
And then maybe it's no longer enough for the online store to just show an image, a price and a buy button?
The buddy
There you have the entrance to the next part.
Because when artificial intelligence changes how customers search, ask, compare and choose...
We must ask a very fundamental question:
What does an online store actually sell?
Magne
I suspect the answer is not just "products".
The buddy
Heh heh.
We'll have to find out.
For friend...
We have only just begun.
Academic background and further reading
This series also builds on my own professional journey through the Web Design study , the eMarketing study , Innovation and Commercialization and professional seminars in San Francisco and Oxford . Here you will find the background, professional environments and experiences that have followed the development from the early years of the web to today's digitalization.
Recommended literature
Developments in artificial intelligence are moving faster than perhaps any other field of study in our time. No single book can provide all the answers, but good books can provide a solid foundation for understanding the technology, the opportunities, and the challenges.
In the KI-Kompis series, we therefore recommend a selection of books that illuminate artificial intelligence from different perspectives – technology, strategy, management, innovation, ethics, digitalization and practical application. Together, they provide a broader understanding of how artificial intelligence affects people, businesses and society.
Click on the book icon to see the full literature overview with recommended books on artificial intelligence.
Recommended books from our library
Co-Intelligence: Living and Working with AI
Author: Ethan Mollick
Short review
One of the most recommended introductory books on artificial intelligence. Ethan Mollick explains in an easy-to-understand way how humans and AI can collaborate, what opportunities the technology offers, and why understanding artificial intelligence is becoming an important skill for everyone – not just technologists.
Why we recommend the book
This is one of the best books to start with if you want a practical and understandable introduction to artificial intelligence. It is a perfect fit as the first book in our blog series about AI.
The AI Advantage: How to Put the Artificial Intelligence Revolution to Work
Author: Thomas H. Davenport
Short review
A practical and insightful book that shows how artificial intelligence can be used to create value in businesses. Thomas H. Davenport combines research and concrete examples to explain how AI can improve decisions, streamline work processes, and contribute to innovation.
Why we recommend the book
This book is well suited for managers, employees, and decision-makers who want to understand how artificial intelligence can be used in practice. It bridges the gap between technology and value creation, and therefore fits well with the philosophy behind The Invisible Capital.
Artificial Intelligence: A Guide for Thinking Humans
Author: Melanie Mitchell
Short review
Melanie Mitchell provides a balanced and easy-to-understand introduction to what artificial intelligence is, how the technology works, and what limitations it still has. The book distinguishes between myth and reality and makes complex topics accessible to a wide audience.
Why we recommend the book
This book is perfect for those who want a deeper understanding of artificial intelligence without having to be a programmer or data scientist. It complements the other recommendations by placing today's AI developments in a larger academic and historical perspective - exactly the understanding we want to build through this blog series.
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