Ontology: What Exists and What AI Can Represent

Ontology Is an Account of What There Is

At its broadest, ontology asks:

What exists, what kinds of things exist, and what basic structures relate them?

An inventory is only the beginning. An ontology must also address whether objects, events, properties, relations, numbers, institutions, and mental states exist in the same way. It must say what makes an entity the same entity through change and whether some things depend on other things for their existence.

A rock and a corporation can both be treated as entities. The rock persists through a physical organization of matter. The corporation depends on legal rules, records, offices, agreements, and collective practices. Both can be real without having the same mode of existence or the same conditions of identity.

Ontology therefore concerns more than a hidden substance behind appearances. It studies the inventory, categories, identity conditions, dependence relations, and general structure of a world.

The Term and Its Scope

The word ontology combines Greek roots associated with being or that which is, and with study or account. The discipline is often introduced through the question “What is there?” Yet contemporary ontology also investigates the most general features of what there is and the ways different entities relate. The term itself became a disciplinary label in early modern philosophy rather than arriving unchanged as an ancient Greek field name. Stanford Encyclopedia of Philosophy: Metaphysics

This broad scope matters because ontology is sometimes reduced to one of two narrower projects:

• a search for the ultimate material from which everything is made;
• a search for the essential definition of each kind of thing.

Both can raise ontological questions, but neither exhausts the field. Ontology also asks whether events are entities, whether properties can exist independently of their instances, how social institutions depend on collective practices, and what makes a person or organization persist over time.

Four Dimensions of an Ontology

Inventory: what does the theory admit?

Every theory relies on some account of what it takes to be real or indispensable to explanation. A physical theory may quantify over fields and particles. A theory of mind may include experiences, beliefs, intentions, or functional states. A social theory may refer to institutions, norms, roles, and collective agents.

Using a noun does not automatically settle the ontology. A theory may speak conveniently about an “average consumer” without treating that consumer as an individual entity. The ontological question is whether the theory is committed to such a thing as part of its account of the world.

This is why ontology includes the study of ontological commitment: what must exist, or be treated as existing, if a set of claims is true?Stanford Encyclopedia of Philosophy: Logic and Ontology

Categories: what kinds of entities are there?

Ontologies distinguish among general kinds:

There is no universally accepted final table. The dispute is partly about whether these divisions are discovered in reality, imposed by language and cognition, or produced through an interaction between the world and a purpose of inquiry.

Identity: what makes something the same thing?

Identity through change is a central ontological problem.

• A person normally remains the same person after changing a name.
• A corporation may survive a complete change of staff.
• An essay may retain its identity across revisions because a version history connects them.
• A product may acquire new packaging without becoming a new model.
• A machine-learning system may be called the same product after an update even when the deployed model artifact has changed.

The answer depends on the kind of entity under discussion. Legal continuity can matter for a corporation, biological continuity for an organism, provenance for a document, and a stable identifier plus version policy for a software artifact. A matching name is not enough to establish identity, and a changed property does not by itself establish a new entity.

Dependence and structure: what relies on what?

Some entities appear capable of existing independently; others exist only through a supporting structure.

A purchase depends on participants, an object of exchange, a time, and a transaction. A currency depends on institutional rules and practices. A software process depends on code and an execution environment. A fictional character can exist as an object of a story and discourse without being an actual historical person.

These examples separate three claims that are often conflated:

an expression refers to something
≠ a model represents it as an entity
≠ a corresponding entity exists in the actual world

An ontology has to state which level it is describing.

Ontology, Essence, Epistemology, and Metaphysics

Ontology overlaps with several neighboring subjects but answers a distinct question.

Essence concerns what an entity must be in order to count as the kind of thing it is. Ontology also asks whether that kind exists, how its instances persist, and how it relates to other kinds.

Epistemology concerns knowledge, evidence, and justification. Whether intentions are mental states is an ontological issue. How an AI system can infer an intention from language and behavior is an epistemological and methodological issue. A complete inquiry needs both: an account of the target and an account of the evidence for finding it.

Metaphysics is usually broader. It includes ontology but also ranges over causation, time, modality, freedom, grounding, and the overall structure of reality. The borders vary across traditions, so ontology should not be treated as a universally fixed department inside metaphysics.

Computational Ontology: Making Commitments Explicit

In knowledge representation, ontology names an engineered artifact as well as a philosophical inquiry.

Thomas Gruber’s influential formulation describes an ontology as a specification of a conceptualization. The practical aim was knowledge sharing: agents need an explicit vocabulary defining the classes, relations, functions, and other objects used in a shared domain.A Translation Approach to Portable Ontology Specifications

A computational ontology can therefore be understood as:

an explicit, inspectable account of the entity types, properties, relations, and constraints that a system recognizes within a domain.

Consider a commerce domain:

Customer ──has──> Need
Brand ──offers──> Product
Merchant ──sells──> MarketOffering
MarketOffering ──realizes──> Product
Customer ──buys──> MarketOffering
Customer ──uses──> Product
Order ──contains──> MarketOffering

The arrows do not settle the model. Designers still have to decide:

• Is a product a design, a model, a service capability, or an individual item?
• Does price belong to the product, the market offering, or a time-bounded quote?
• Are two merchants selling one product or two distinct offerings?
• Does an offering cease to exist when it is withdrawn?
• Does a purchase establish that a customer’s need was met?

These are ontological choices with operational consequences. They determine how data can be joined, which inferences are valid, and what an automated system can act upon.

What an Engineering Ontology Contains

Classes and individuals

A class represents a kind such as Person, Organization, or Product. An individual represents a particular member of one or more classes. In OWL 2, classes can be understood as sets of individuals, while properties express relations among individuals or between individuals and data values.W3C: OWL 2 Primer

Class membership need not be exclusive. One individual might be both a customer and an employee. A useful ontology states when multiple classifications are compatible and when classes are disjoint.

Properties and relations

Properties express information such as a name, status, date, or quantity. Relations connect entities through claims such as worksFor, owns, published, or purchased.

Whether a value should become an entity depends on the required reasoning. A city can be stored as a string if nothing else refers to it. It should usually become an identified entity if the system must connect it to regions, offices, time zones, routes, or multiple records.

Axioms and constraints

An ontology can state more than permitted vocabulary. It can encode claims that support validation and inference:

• every order has a buyer;
• a cancelled order cannot simultaneously be awaiting payment;
• every researcher is an employee;
• people and organizations are disjoint kinds;
• a relation is symmetric, transitive, functional, or inverse to another relation.

Formal languages differ in what they express and in the assumptions they make. An ontology should therefore be judged partly by the reasoning task it must support, not by the size of its vocabulary.

Identity, provenance, and time

Operational systems encounter aliases, duplicate records, namesakes, revisions, and claims that change over time. An ontology alone does not solve entity resolution. It has to work with identifiers, provenance, temporal data, and confidence to answer:

• Do two records refer to the same entity?
• When was a property true?
• Which source supports the assertion?
• Is the assertion observed, inferred, or supplied by a user?

Without these distinctions, a clean graph can still represent an incorrect world.

An Ontology Is More Than a Taxonomy or Schema

A taxonomy arranges categories, often in a hierarchy. It tells a system that a researcher is a kind of employee or that a phone is a kind of product.

A database schema specifies storage: tables, columns, keys, and types.

An ontology addresses semantics: what the records represent, which relations are meaningful, what identity means, and which constraints or inferences follow.

The three can overlap, but they are not interchangeable:

taxonomy: how kinds are classified
schema: how data is stored
ontology: what the modeled things mean and how they may relate

A relational database can implement an ontology. A graph database can also lack one. The storage technology does not settle whether the domain has been conceptually defined.

Does a Large Language Model Have an Ontology?

Language models learn many implicit categories and relations from text. They can produce statements such as “a company releases a product” and often resolve an ambiguous word from context. This behavior shows useful conceptual organization.

It does not establish that the model contains one explicit, stable, internally consistent ontology. Information about a company or product is distributed across parameters and contextual activations. Different prompts can elicit incompatible classifications or identity assumptions.

Typical failures include:

• treating a brand and its owning company as one entity;
• treating a product and a purchasable offering as one object;
• confusing a name with a verified identity;
• confusing a discourse entity with an actually existing entity;
• applying different identity criteria across contexts.

For reliable action, an AI system often needs several layers:

language model
+ domain ontology
+ current entity and state data
+ provenance and time
+ permissions and action rules

The language model interprets variable expression. The ontology constrains the objects and relations the system recognizes. Data supplies current instances and states. Authorization determines which actions are permitted. Understanding “update the essay” is insufficient until the system identifies the essay, language edition, revision, destination, and authority to publish.

Are Ontologies Discovered or Designed?

Every practical ontology is selective. The same cup of coffee can be modeled as:

• a physical object with a temperature and container;
• a sellable item with a price and inventory status;
• a nutritional object with ingredients and calories;
• an experience with flavor and context;
• an environmental object with a supply chain and emissions.

Different purposes can justify different boundaries. Purpose, however, does not make every model equally adequate. Merging two different people, treating a temporary quote as a permanent product price, or discarding provenance can produce false claims and harmful actions.

A useful ontology can be examined through seven questions:

1. What problem is the ontology intended to support?
2. Which entities does it recognize, and which does it omit?
3. Are identity conditions clear for each important kind?
4. Are objects, events, states, properties, and relations kept distinct?
5. Do the axioms support the required inferences without producing contradictions?
6. Can the model represent time, provenance, and uncertainty where they matter?
7. Can old data still be interpreted when the ontology changes?

An ontology is a map of what a system takes to exist and how those things fit together. It is answerable to reality, but it should not be confused with reality itself.

Entities Are Local; Ontology Is the Framework

An entity is something a system can identify, describe, relate, track, or act upon. An ontology states:

• what can count as an entity;
• which kinds of entity exist in the domain;
• what makes entities identical or distinct;
• which properties they can bear;
• which relations and events they can enter;
• which conditions constrain their existence and change.

The relationship can be summarized in one sentence:

An entity is one recognized item; an ontology is the system’s account of what kinds of items may be recognized and how they are organized.