What is knowledge representation in AI?

Knowledge representation meaning in plain words

Knowledge representation means choosing a format for what you know so that software can use it, not only store it. A sentence in a document is knowledge for a person; for a program it is a string of characters until someone, or something, turns it into facts and rules with a fixed shape.

It is one of the oldest parts of symbolic AI, and standard AI textbooks give it its own chapters beside logic and inference. The field is often called knowledge representation and reasoning, KR&R for short, because the two questions cannot be separated: how you write knowledge down limits what any reasoning step can do with it.

Types of knowledge representation

The main types of knowledge representation are logic, rules, frames, semantic networks and graphs, and ontologies, each suited to a different kind of question. Machine learning adds a further, implicit kind: what a trained model knows is held as numbers in its weights and in embeddings, not as statements anyone wrote.

• Logic: precise statements about objects and relations, as in first-order logic, that a program can check and chain.
• Rules: if-then statements, such as 'if the account is in Germany, route it to the DACH team'.
• Frames: a template for one kind of thing with named slots, such as an account with industry, size and owner.
• Semantic networks and knowledge graphs: entities joined by named relationships a program can follow.
• Ontologies: shared definitions of the kinds of things in a domain and how they may relate.

Knowledge representation examples from sales

A good knowledge representation example is the gap between a free-text note and a structured record. The note 'they're probably mid-market, sell in Germany' is useful to the rep who wrote it, but a program can do little with it beyond searching for the words.

The same knowledge written as fields (employees: 320; market: DE; segment: mid-market) can drive a routing rule, a filter for a campaign or a check that every mid-market account has an owner. Nothing new was learned; the knowledge was only written in a form a program can act on. The figures here are illustrative.

Why the choice of form decides what can be inferred

The form decides what can be inferred because a program can only reason with the distinctions the representation makes. If a record has no field for who reports to whom, no rule can work out the buying committee from it, however clever the rule.

There is also a trade-off between expressiveness and effort. Richer forms, such as full logic, can state more but are slower to reason over and harder to maintain; simpler forms, such as tables, are fast and familiar but say less. Much research on combining neural and symbolic methods, the neurosymbolic research line, is about pairing the implicit knowledge in a model with explicit representations that can be inspected.

What it means for a team using AI

For a team using AI, knowledge representation is the practical habit of writing down what the business knows on purpose, in named, reviewed places. A model can read prose, so the format need not be formal, but separate sections for positioning, ideal customer, offerings and tone are easier to check, update and point an assistant at than one long document.

It also helps to know which facts should be structured. Firmographics, territories and deal stages belong in fields a rule can read; arguments, stories and voice can stay as prose.