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What are the different types of knowledge?

Most people don't think about knowledge having different types. You know things, you learn things, you forget things. But philosophers and cognitive psychologists have spent centuries drawing distinctions that turn out to be surprisingly useful once you start thinking about how you actually learn, teach, or manage information.

The reason it matters practically: different types of knowledge need to be captured, stored, and retrieved differently. The way you record a fact is not the way you record a skill. The way you preserve an insight is not the way you preserve a procedure. Most note-taking and knowledge management systems treat everything the same way, which is why they work well for some things and badly for others.

Here's a map of the main types, how they relate to each other, and what each one means for how you work.

Tacit, explicit, and implicit knowledge

This is the most important set of distinctions in knowledge management, and the first two come from the work of Michael Polanyi, a Hungarian-British polymath who wrote about them in the 1950s and 60s.

Explicit knowledge is knowledge that can be written down, spoken aloud, and transferred to someone else through language. The capital of France is Paris. Water boils at 100°C at sea level. The company's refund policy allows returns within 30 days. This is the kind of knowledge that lives comfortably in notes, documents, databases, and textbooks. It can be searched, copied, and shared without losing much in the transfer.

Tacit knowledge is knowledge you have but can't easily put into words. How to ride a bicycle. How to recognise a good design from a bad one. The feeling that tells an experienced doctor something is wrong before the test results arrive. Polanyi's formulation was that we can know more than we can tell. You know how to do it, but if someone asks you to explain exactly how, the words don't capture the thing itself. Tacit knowledge is typically acquired through experience, practice, and immersion rather than through instruction.

Implicit knowledge sits between the two, and the distinction is worth knowing. Implicit knowledge is knowledge you haven't articulated but could if someone asked you. The unwritten norms of your workplace. The patterns you've noticed in customer behaviour but never written down. The way you instinctively structure a presentation. Unlike tacit knowledge, which resists articulation, implicit knowledge just hasn't been made explicit yet. With effort, it can be documented, shared, and scaled.

The distinction matters for personal and organisational knowledge management because most systems are built almost entirely around explicit knowledge. Your notes app captures what you can write down. Your research library stores documents and articles. Your search finds things you've recorded in words.

Tacit knowledge is much harder to capture. The best you can do is create approximations: descriptions of how something felt, records of decisions and the reasoning behind them, research logs that track what you expected versus what happened. These aren't the tacit knowledge itself, but they're the closest external representation you can store, and they're far better than nothing.

Implicit knowledge is the biggest opportunity in most organisations. It's the knowledge that's locked in people's heads, could be written down, and would be enormously valuable to others if it were. Making implicit knowledge explicit, through documentation, templates, checklists, and shared wikis, is often the highest-leverage knowledge management activity a team can do.

The SECI model

The Nonaka-Takeuchi SECI model, developed in the 1990s for organisational knowledge management, describes how tacit, implicit, and explicit knowledge convert into each other through four processes: socialisation (tacit to tacit, through shared experience), externalisation (tacit to explicit, through articulation), combination (explicit to explicit, through organising and connecting), and internalisation (explicit to tacit, through practice).

The cycle matters for individual learning too. Writing things down (externalisation) and then practising them (internalisation) is how much individual learning actually works. Your notes are the externalisation step. Your practice is the internalisation step.

Declarative, procedural, and conditional knowledge

This set of distinctions comes from cognitive psychology and educational research, particularly the work of Anderson and Krathwohl in their 2001 revision of Bloom's Taxonomy.

Declarative knowledge is knowing what. Facts, concepts, propositions, definitions. Paris is the capital of France. Photosynthesis converts light energy to chemical energy. DNA has a double helix structure. Declarative knowledge answers factual questions and can be stated in sentences. It's what most education tests for and what most note-taking systems are designed to capture.

Procedural knowledge is knowing how. How to perform long division. How to conduct a job interview. How to write a literature review. Procedural knowledge is embedded in actions and sequences rather than in facts, and while it can be partially documented (as a recipe, a checklist, a standard operating procedure), the documentation never fully captures the skill. You can read a recipe and still cook badly, because the procedural knowledge of timing, heat management, and improvisation isn't in the recipe.

Conditional knowledge is knowing when and why. This is the type most people overlook, and it's often the most valuable. Conditional knowledge is the ability to recognise which situation calls for which declarative or procedural knowledge. A doctor knows the symptoms of many diseases (declarative). They know how to run diagnostic tests (procedural). Conditional knowledge is what tells them which tests to run for this particular patient, given these particular symptoms, at this particular stage. It's the judgment layer that sits on top of the other two.

Conditional knowledge is what separates experts from competent practitioners. Both might have the same factual and procedural knowledge, but the expert knows when to apply which approach and why. This is also the type of knowledge most difficult to teach directly, since it develops through extensive practice and exposure to varied situations.

In educational terms, these three types map loosely onto Bloom's Taxonomy: declarative knowledge corresponds to the lower levels (remember, understand), procedural to the middle levels (apply, analyse), and conditional to the highest levels (evaluate, create). A student study system that only practises recall of facts is building declarative knowledge. One that practises application and judgment is building all three.

Factual vs conceptual knowledge

Krathwohl's 2002 revision of Bloom's Taxonomy makes a further distinction within declarative knowledge that's worth knowing.

Factual knowledge is the basic elements: terminology, specific details, isolated pieces of information. The boiling point of water. The date of the French Revolution. The syntax of a Python function. These are the building blocks, often memorised, and usually the starting point for learning a new domain.

Conceptual knowledge is knowing the relationships between those facts, how they connect, organise, and form larger structures. Understanding why water boils at different temperatures at different altitudes (the relationship between atmospheric pressure and phase changes) is conceptual. Knowing that 100°C is the boiling point at sea level is factual. Understanding how different programming paradigms relate to each other and when each is appropriate is conceptual. Knowing the syntax of a for-loop is factual.

The distinction matters for learning because factual knowledge can be acquired through memorisation, but conceptual knowledge requires understanding. You can memorise facts without understanding them, and you can understand concepts without remembering all the specific facts. The best learning builds both: enough facts to ground the concepts, enough conceptual understanding to make the facts meaningful and memorable.

For note-taking and knowledge management, factual knowledge is well-served by reference systems and search. Conceptual knowledge benefits from linking, mapping, and writing in your own words, which is why systems like Zettelkasten and evergreen notes are built around concept-oriented notes rather than fact-oriented ones.

Empirical vs rational knowledge

Empirical knowledge (also called a posteriori knowledge) comes from observation and experience. You know that ice is cold because you've touched it. You know that a particular approach doesn't work because you've tried it. This is the knowledge that accumulates through doing things, running experiments, making mistakes, and paying attention to results.

Rational knowledge (also called a priori knowledge) is derived through reasoning and logic, independent of specific experience. Mathematical proofs, logical deductions, and theoretical frameworks are rational knowledge. You know that 7 + 5 = 12 not because you've counted twelve objects but because you understand the logical relationships between numbers.

Most practical work involves both. You might have a theoretical framework (rational) that you test against observations (empirical), and the interaction between the two is where genuine understanding develops. The research workflow is essentially a formalised version of this loop: develop a hypothesis (rational), test it against evidence (empirical), update your understanding.

Empirical knowledge is particularly valuable to capture because it's uniquely yours. Many people know the theory. Fewer have the specific observations and hard-won insights that come from applying it in a particular context. A research log that documents what you predicted, what you observed, and what you learned is a record of empirical knowledge that compounds over time.

Domain-specific vs general knowledge

Domain-specific knowledge applies to a particular field, discipline, or context. Knowing how to debug a React application. Understanding the precedents in UK employment law. Being able to identify a particular species of mushroom by its cap shape.

General knowledge transfers across domains. Critical thinking, statistical reasoning, project management, clear writing. These skills apply regardless of the specific subject matter.

The distinction matters for how you organise your knowledge. Domain-specific knowledge benefits from being grouped by domain: your notes on a particular topic together so you can see the full picture. General knowledge benefits from being accessible everywhere: a principle about clear thinking that you captured while studying one field might be equally relevant to a completely different project.

This is one reason why concept-oriented note-taking systems like Zettelkasten and evergreen notes organise by concept rather than by source or project. A note about "confirmation bias" is relevant to psychology research, investment decisions, and product design. Organising it under any one domain hides it from the others. Organising it by concept, and linking it to all three contexts, makes general knowledge available wherever it's needed.

Semantic search addresses this from a different angle: rather than requiring you to file things in the right conceptual location, it finds relevant knowledge based on meaning regardless of where you stored it.

Tribal and institutional knowledge

Tribal knowledge is the informal, undocumented understanding that exists within an organisation or team but has never been written down. Why we do things this way. Who to ask about that system. The workaround for the bug that nobody ever fixed. The context behind why a decision was made two years ago.

This is a specific type of implicit knowledge that's uniquely valuable and uniquely fragile. When the person who holds it leaves the company, the knowledge leaves with them. When a team member is unavailable, the knowledge is temporarily inaccessible to everyone else.

Converting tribal knowledge into documented, searchable, shared knowledge is one of the highest-value activities in organisational knowledge management. Shared workspaces, team wikis, and documented processes make tribal knowledge explicit and survivable. An AI that can search across your team's accumulated documentation makes retrieving that knowledge practical even when the person who wrote it down isn't available.

Meta-knowledge

There's one more type worth understanding: knowledge about your own knowledge. What do you know? What don't you know? Where are the gaps? How reliable is your understanding in a given area?

Psychologists call this metacognition, and Krathwohl included it as the fourth type in his taxonomy (alongside factual, conceptual, and procedural). It turns out to be one of the strongest predictors of effective learning and decision-making. People who accurately assess what they know and don't know make better decisions than those who are overconfident or underconfident in their knowledge.

The practical application: your system should help you see not just what you've captured but what's missing. A literature review that maps the landscape of a field reveals gaps in your reading. A canvas that lays out your current thinking on a topic makes weak points visible. The weekly review forces regular reckoning with what you've accomplished and what you've been avoiding.

The Zettelkasten tradition treats this as one of the system's most valuable features: a mature Zettelkasten doesn't just store knowledge. It reveals what you know and what you don't by showing where your notes are dense (strong understanding) and where they're sparse (gaps that need attention).

Why this matters for how you manage knowledge

Different types of knowledge need different approaches:

Explicit, declarative, and factual knowledge is easy to capture: write it down, file it, search for it later. Most tools handle this well.

Conceptual knowledge benefits from connection and context: linking notes together, writing in your own words, building maps and frameworks that show how ideas relate.

Procedural knowledge needs a different format: checklists, templates, step-by-step guides, documented workflows.

Conditional knowledge develops through practice and reflection: research logs that record predictions and outcomes, case studies, and deliberate exposure to varied situations.

Tacit and implicit knowledge require deliberate externalisation: writing things down in your own words, recording your reasoning and predictions, capturing the "why" behind decisions rather than just the decisions themselves.

Tribal knowledge needs to be made explicit and shared: wikis, onboarding documentation, and searchable team libraries.

And meta-knowledge emerges from regular review and from building a system rich enough to show you its own gaps.

No single tool or method handles all of these perfectly. But understanding which type of knowledge you're dealing with in any given moment helps you choose the right approach for capturing, storing, and using it.

Frequently asked questions

What's the difference between tacit and implicit knowledge? Tacit knowledge is knowledge you have but can't articulate at all, like how to balance on a bicycle. Implicit knowledge is knowledge you haven't articulated but could if asked, like the unwritten norms of your workplace. The practical difference: implicit knowledge can be made explicit with effort (documentation, interviews, process mapping). Tacit knowledge often can't be, or at least not fully, because the knowing is inseparable from the doing.

How do the different types relate to Bloom's Taxonomy? Bloom's Taxonomy describes levels of cognitive complexity: remember, understand, apply, analyse, evaluate, create. These levels correspond roughly to knowledge types. Remembering factual knowledge is the lowest level. Understanding conceptual knowledge requires higher-order thinking. Applying procedural knowledge involves the middle levels. Evaluating and creating require conditional and metacognitive knowledge, the ability to judge which knowledge to apply and to monitor your own understanding.

Which type of knowledge is most important? It depends on the context. For academic study, declarative and conceptual knowledge dominate assessments. For skilled trades and crafts, procedural and tacit knowledge matter most. For research, empirical knowledge and the ability to generate new understanding from observation are central. For leadership, conditional knowledge (knowing when to apply which approach) and tacit knowledge about people and situations are often more valuable than any amount of factual knowledge.

How do I capture tacit knowledge? You can't capture it directly, but you can capture useful approximations. Write about your decision-making process: why you chose one approach over another, what your intuition was telling you, what you noticed that others didn't. Record your reasoning before and after important decisions using a research log. Over time, these records create a partial externalisation of tacit knowledge that your future self and colleagues can learn from.

What is tribal knowledge and why does it matter? Tribal knowledge is the informal, undocumented understanding within an organisation: why things are done a certain way, who to ask about specific systems, the history behind key decisions. It matters because when people who hold it leave, the knowledge disappears. Making tribal knowledge explicit through documentation, shared workspaces, and team wikis is one of the most valuable things a team can do to preserve institutional memory.

How does this relate to note-taking and knowledge management systems? Most systems are optimised for explicit, declarative knowledge: facts, quotes, and information you can write down. Systems like Zettelkasten and evergreen notes push toward capturing conceptual and relational knowledge by requiring you to write in your own words and link ideas together. Checklists and templates capture procedural knowledge. Research logs capture empirical and conditional knowledge. The best practice varies depending on which type of knowledge you're primarily working with.