What Is Knowledge?

If you are going to build a system for managing knowledge, it seems reasonable to start by figuring out what knowledge actually is. This turns out to be one of the oldest and most stubbornly difficult questions in all of philosophy, which should give you some indication of why knowledge management systems so often disappoint.

Philosophers have been arguing about the nature of knowledge since at least the fifth century BCE, when Plato had Socrates corner various Athenians into admitting they did not know what they thought they knew. Twenty-five centuries later, there is still no consensus. This is either deeply embarrassing for philosophy or a testament to the genuine difficulty of the question, depending on your temperament.

For our purposes, we do not need to resolve the debate — that would require a different book and considerably more hubris. What we need is a working understanding of the major positions, because each one illuminates something important about how knowledge should be captured, represented, and retrieved in a practical system. The philosophy is not a detour. It is the foundation.

The Classical Definition: Justified True Belief

The standard starting point is the definition usually attributed to Plato, though what Plato actually said is more complicated than the textbook version suggests. In the Theaetetus, Socrates and his interlocutors explore and ultimately reject several definitions of knowledge, but the one that stuck in the Western tradition is this: knowledge is justified true belief.

Unpack that, and you get three necessary conditions. For you to know that something is the case — say, that water boils at 100 degrees Celsius at sea level — three things must all be true:

1. Belief: You must believe the proposition. If you do not believe that water boils at 100°C, you cannot be said to know it, even if it happens to be true. Knowledge requires a knower.

2. Truth: The proposition must actually be true. You can believe with absolute conviction that the earth is flat, but that does not make it knowledge. False beliefs are just false beliefs, no matter how sincerely held.

3. Justification: You must have good reasons for your belief. If you believe that water boils at 100°C because a fortune cookie told you so, and it happens to be correct, that is not knowledge — that is a lucky guess. You need evidence, reasoning, or some other form of epistemic warrant.

This definition — often abbreviated as JTB — has an elegant simplicity. It distinguishes knowledge from mere true belief (which could be accidental) and from justified but false belief (which, however well-reasoned, is still wrong). For roughly 2,400 years, most Western philosophers considered it essentially correct, or at least a reasonable starting point.

Then, in 1963, a three-page paper blew the whole thing up.

The Gettier Problem

Edmund Gettier was a young philosopher at Wayne State University who, by the account of colleagues, published his famous paper largely because he needed a publication for tenure. The paper, "Is Justified True Belief Knowledge?", is one of the shortest and most devastating in the history of philosophy. It presents two counterexamples that show, with ruthless clarity, that justified true belief is not sufficient for knowledge.

Here is the structure of a Gettier case, stripped to its essentials. Suppose you have a justified belief in some proposition P. Suppose P is, in fact, false — but through some coincidence, a related proposition Q, which you infer from P, happens to be true. You now have a justified true belief in Q, but nobody in their right mind would call it knowledge.

Gettier's original examples involve job candidates and coin-counting, but a cleaner illustration goes like this. You are driving through the countryside and see what appears to be a barn. You form the justified belief: "There is a barn in that field." Your belief is justified because your eyes are working, the lighting is good, and the object looks exactly like a barn. And there is, in fact, a barn in that field. But — unbeknownst to you — the entire county is filled with elaborate barn facades, Hollywood-style fake fronts propped up for some unspecified reason. By sheer luck, the one you happened to look at is the only real barn in the area.

You have a justified true belief that there is a barn in that field. But do you know it? Your justification — visual perception — would have led you to the same belief in front of any of the fakes. You got the right answer by accident, even though your reasoning process was perfectly sound.

This is deeply unsettling, and not just for philosophers. If you are building a knowledge base, you are implicitly making claims about what is known. The Gettier problem tells you that even well-justified, true entries in your knowledge base might not constitute genuine knowledge if the justification is unreliable in the broader context. A piece of information can be correct and well-sourced and still not be knowledge in any robust sense if the process that produced it would have produced the same result even if it were false.

Responses to Gettier: JTB+ Theories

The philosophical community's response to Gettier was, roughly: panic, followed by decades of increasingly baroque attempts to patch the JTB definition. These attempts generally take the form "knowledge is justified true belief plus some additional condition." Hence, JTB+ theories.

The No-False-Lemmas Condition

The simplest fix: add the requirement that your justification must not depend on any false beliefs. In the barn case, your justification implicitly relies on the false belief that the county is not full of fake barns. Rule out reasoning chains that pass through falsehoods, and many Gettier cases dissolve.

The problem is that this condition is both too strong and too weak. Too strong because much of our reasoning does pass through approximations and simplifications that are, strictly speaking, false. Too weak because philosophers have constructed Gettier cases that do not involve any false lemmas at all. (Philosophers are very good at constructing counterexamples. It is essentially their core competency.)

The Defeasibility Condition

A more sophisticated approach: your justification must be undefeated — there must be no true proposition that, if added to your evidence, would undermine your justification. In the barn case, the proposition "this county is full of fake barns" would defeat your justification for believing you see a real one. Since that defeater exists and is true, you do not have knowledge.

This works better, but defining "defeat" precisely turns out to be fiendishly difficult. Some true propositions are misleading defeaters — they would undermine your justification even though your belief is, in fact, well-founded. Distinguishing genuine from misleading defeaters requires something very close to the concept of knowledge itself, which makes the definition uncomfortably circular.

Causal Theories

Perhaps knowledge requires an appropriate causal connection between the belief and the fact that makes it true. You know there is a barn because the actual barn caused your visual experience. In Gettier cases, the causal chain is broken or deviant — the real barn is not the right kind of cause of your belief.

Causal theories work reasonably well for empirical knowledge but stumble on mathematical and logical knowledge. What causes your belief that 2 + 2 = 4? The number 2 does not cause anything; it is not the kind of thing that participates in causal chains. Unless you adopt a very unusual philosophy of mathematics, causal theories cannot account for a large chunk of what we ordinarily call knowledge.

Reliabilism

Alvin Goldman proposed a different approach entirely: forget about justification as the knower experiences it, and focus instead on the reliability of the process that produces the belief. A belief counts as knowledge if it is true and was produced by a reliable cognitive process — one that tends to produce true beliefs in the relevant circumstances.

This has considerable appeal. It explains why perception counts as a source of knowledge (it is generally reliable) and why reading tea leaves does not (it is not). It handles the barn case neatly: your perceptual process is not reliable in fake-barn county, because it would produce false beliefs most of the time, so your true belief about the one real barn does not count as knowledge.

Reliabilism also maps well onto thinking about knowledge systems. When you evaluate a source for your knowledge base, you are implicitly assessing its reliability — the process by which the information was produced. Peer-reviewed research is more reliable (in general) than blog posts, which are more reliable (in general) than random social media comments. Not because of anything intrinsic to the format, but because the processes that produce them have different track records of generating true beliefs.

The main objection to reliabilism is the generality problem: any belief-forming process can be described at many levels of generality, and the reliability of the process depends on how you describe it. "Visual perception" is highly reliable. "Visual perception of barns in fake-barn county" is not. "Visual perception of barns in fake-barn county on a Tuesday" could go either way. There is no principled way to pick the right level of description, which means reliabilism cannot give a determinate answer about whether a given belief is knowledge.

Knowledge as a Mental State

Timothy Williamson has argued, influentially, that knowledge is not analyzable into more basic components at all. Knowledge is a factive mental state — a mental state that guarantees the truth of its content — and it is more fundamental than belief, not built out of it. On this view, trying to define knowledge in terms of belief plus additional conditions is like trying to define the color red in terms of some other color plus additional features. Knowledge is basic. You either know or you do not.

This view has the advantage of avoiding Gettier problems entirely (since it does not attempt a reductive analysis), but it is not very helpful if you are trying to build a knowledge base. You cannot peer into someone's mental state to determine whether they are in a state of knowledge or merely belief. What you can do is assess the evidence, check the sources, and evaluate the reliability of the process — which brings us back to something like reliabilism in practice, whatever the correct metaphysics turns out to be.

Knowledge as Information

There is a different tradition, more common in computer science and information theory than in philosophy, that treats knowledge as a species of information — specifically, information that has been processed, contextualized, and integrated into a framework that makes it useful for decision-making or action.

On this view, the philosophical questions about justification and truth are less important than the practical question of utility. Knowledge is information you can act on effectively. This is pragmatic in the best sense, and it is the implicit philosophy behind most knowledge management systems. When you add something to your knowledge base, you are not typically making a metaphysical claim about justified true belief. You are making a practical judgment: this information, in this context, is useful enough to be worth preserving and retrieving.

The danger of this view is that it collapses the distinction between knowledge and information, which — as we will see in Chapter 4 — is a distinction worth preserving. Not all information is knowledge, and treating it as such leads to bloated, low-signal knowledge bases where the useful stuff is buried under mountains of trivia.

Three Kinds of Knowledge

Regardless of how you define knowledge in the abstract, there is a practical taxonomy that dates back to Bertrand Russell and has proven remarkably durable. There are (at least) three fundamentally different kinds of knowledge, and each requires different strategies for capture and representation.

Propositional Knowledge (Knowing That)

This is knowledge of facts: knowing that Paris is the capital of France, that water is H₂O, that quicksort has O(n log n) average-case complexity. Propositional knowledge is the easiest kind to represent in a knowledge base because it can be stated in declarative sentences. It is what most people think of when they think of knowledge, and it is what most knowledge systems are designed to handle.

But even propositional knowledge is not as straightforward as it seems. Facts do not exist in isolation; they are embedded in webs of relationships and dependencies. Knowing that quicksort has O(n log n) average-case complexity is much more useful if you also know what circumstances produce the worst case, how it compares to mergesort, and when you should use one versus the other. A knowledge base that stores isolated facts without capturing their relationships is a trivia database, not a knowledge system.

Procedural Knowledge (Knowing How)

This is knowledge of how to do things: knowing how to ride a bicycle, how to debug a segmentation fault, how to conduct a job interview. The philosopher Gilbert Ryle drew a sharp distinction between "knowing that" and "knowing how" in his 1949 book The Concept of Mind, arguing that procedural knowledge cannot be reduced to a set of propositions.

Consider debugging. An experienced developer's debugging process involves pattern recognition, intuition about likely causes, strategies for isolating variables, and a feel for when to keep digging versus when to back up and try a different approach. You can write some of this down as procedures and heuristics, but the written version always falls short of the actual skill. The gap between the documentation and the competence is precisely the gap between propositional and procedural knowledge.

This has profound implications for knowledge management. If procedural knowledge cannot be fully captured in propositions, then no amount of documentation will transfer expertise completely. The best you can do is create artifacts that support the development of procedural knowledge — tutorials, worked examples, annotated case studies, decision frameworks — while recognizing that the knowledge itself lives in the practitioner, not in the document.

Knowledge by Acquaintance (Knowing What It's Like)

Russell distinguished between knowledge by description (knowing facts about something) and knowledge by acquaintance (direct, experiential knowledge of something). You can read every book ever written about the taste of a mango, but until you have actually tasted one, there is a kind of knowledge you lack.

This category might seem irrelevant to knowledge management — how do you put the taste of a mango into a database? — but it matters more than you might think. Much expert knowledge has an acquaintance component. An experienced systems administrator does not just know facts about how servers behave under load; they have a feel for it, a direct familiarity that informs their judgment in ways they cannot fully articulate. A seasoned designer does not just know principles of visual hierarchy; they have an aesthetic sensibility developed through years of looking at and creating designs.

Knowledge by acquaintance is arguably the hardest kind to manage because it is the most deeply personal and the least transferable through text. But acknowledging its existence — and its importance — is a necessary step toward building knowledge systems that do not pretend all knowledge is propositional.

Why This Matters for Knowledge Bases

If you have made it this far, you might be wondering whether all this philosophy is really necessary. Could we not just get on with building the system and figure out the theory later (or never)?

You could, and many people do. The result is usually a system that works well enough for simple cases and fails in predictable ways for hard ones. Here is how the philosophical framework pays off in practice:

The JTB framework tells you that your knowledge base entries should have three properties: they should reflect genuine beliefs (not aspirational or hypothetical claims mixed in with established facts), they should be true (or at least your best current understanding of the truth), and they should be justified (the source, evidence, or reasoning should be captured alongside the claim). A note without provenance is not knowledge — it is an unsourced assertion.

The Gettier problem tells you to be skeptical of accidental correctness. If a source happens to be right about something but for the wrong reasons, or through a process that is unreliable in general, that should reduce your confidence even if the specific claim checks out. In practice, this means paying attention to the process that generated information, not just the information itself.

Reliabilism tells you that source evaluation is central, not peripheral. The reliability of the process that produced a piece of information is the best proxy you have for whether it constitutes knowledge. Build your system to track and surface provenance.

The three kinds of knowledge tell you that one representation does not fit all. Propositional knowledge can be captured in notes, assertions, and structured data. Procedural knowledge requires tutorials, worked examples, decision trees, and ideally links to practice environments. Knowledge by acquaintance may not be capturable at all, but you can at least point toward the experiences that develop it.

Knowledge-as-mental-state reminds you that knowledge is ultimately in the knower, not in the system. Your knowledge base is not itself knowledgeable. It is a tool that supports your knowledge — your ability to recall, connect, and apply what you have learned. The system succeeds to the extent that it makes you more knowledgeable, not to the extent that it contains more entries.

This last point is worth sitting with. There is a powerful temptation, especially for people who enjoy building systems, to treat the knowledge base as an end in itself — to optimize for comprehensiveness, organization, and aesthetic elegance. These are not bad things, but they are instrumental. The purpose of a knowledge base is to make you better at thinking. If it is not doing that, it does not matter how many notes it contains or how beautifully they are interlinked.

With a working understanding of what knowledge is (and what it is not), we can now turn to the broader question of how knowledge is acquired, validated, and justified. That is the domain of epistemology — the theory of knowledge — and the subject of our next chapter.