Ontology Explained: Why Understanding Existence Matters in Web3 and AI

The Real-World Question Behind Ontology

Before diving into technical definitions, consider this: How do machines understand what a “patient,” “disease,” or “digital asset” actually is? How do blockchain networks verify identity? The answer lies in ontology—the philosophical concept that’s now reshaping how we build technology.

At its core, ontology asks a deceptively simple question: “What really exists?” This ancient philosophical inquiry now underpins modern systems—from semantic webs to decentralized identity layers in blockchain. Understanding ontology isn’t just academic; it directly impacts how AI systems reason, how databases organize information, and how Web3 platforms establish trust.

Understanding Ontology: Beyond the Definition

Ontology comes from the Greek words “onto” (being/existence) and “logy” (study of). In philosophy, it’s the systematic investigation of reality—examining what exists, how to categorize it, and how different things relate to one another.

But here’s where it gets interesting: ontology isn’t just about listing what exists. It’s about defining the structure of existence itself.

Consider these fundamental ontological questions:

• What counts as “real”?
• How do we group things into categories?
• What’s the difference between a universal concept (like “redness”) and a specific instance (like “this red apple”)?
• What relationships exist between entities?

In practical terms, think of ontology as the blueprint for organizing reality. Whether you’re structuring data in a database, building an AI that understands medical terminology, or creating a blockchain identity system—you’re making ontological choices about what exists and how it’s organized.

How Ontology Evolved: From Plato to Modern Technology

The roots of ontological thinking stretch back millennia. Plato and Aristotle debated whether abstract ideas or physical objects were “more real.” Aristotle’s framework—organizing reality into substances, qualities, and relations—became the foundation for Western philosophy.

Fast forward to the 17th century: Christian Wolff formalized “ontology” as a philosophical discipline. By the 20th century, analytic philosophers were interrogating the nature of properties, existence, and language with growing precision.

Today, ontology has transcended pure philosophy. It now shows up in:

• Computer science (structuring knowledge in AI systems)
• Information systems (organizing databases and knowledge graphs)
• Linguistics (semantic web standards)
• Blockchain (defining digital identities and assets)

This evolution reveals something profound: the way we think about reality directly shapes the technologies we build.

The Big Debates: What Actually Counts as Real?

Ontology isn’t settled territory—philosophers and technologists still argue about fundamental questions.

Realism vs. Constructivism

Do categories like “number,” “money,” or “social class” exist independently of human thought? Realists say yes. Constructivists argue these are human inventions, created through agreement or social practice.

This isn’t merely academic. In research, your stance here determines your methodology. If you believe social phenomena are objective facts, you’ll run quantitative experiments. If you see them as socially constructed, you’ll use qualitative interviews and narrative analysis.

Universals vs. Particulars

Does “redness” exist as an abstract concept, or only specific red objects? Does “the number two” have independent reality, or only these two socks on your floor?

In computer science, this debate shows up when you’re designing classification systems. Should your database recognize “employee” as a universal category, or only specific employees as particulars? Your ontology choice affects everything downstream.

Ontology in Research and Social Science

Researchers often don’t realize they’re making ontological assumptions. But they are—constantly.

Two Foundational Approaches:

Objectivist Ontology: Reality exists independently of human perception. Social phenomena have objective existence waiting to be discovered. This approach favors quantitative methods, statistical analysis, and large-scale surveys.

Constructivist Ontology: Reality emerges through human interaction and meaning-making. Social facts are products of collective interpretation. This approach favors qualitative interviews, ethnographic studies, and narrative analysis.

Here’s the practical implication: A researcher studying vaccine hesitancy might design controlled trials (objectivist approach) to measure vaccine efficacy objectively. Another researcher might conduct interviews to understand the lived experiences and beliefs shaping vaccine hesitancy (constructivist approach). Same phenomenon, different ontologies, completely different methodologies.

Before starting any research project, clarify your ontological stance. It determines your methods, your data types, and ultimately, your conclusions.

Ontology vs. Epistemology vs. Methodology: Know the Difference

People constantly conflate these three related but distinct concepts:

Ontology = What exists in the world? (What is real?)

Epistemology = How can we know what exists? (How do we understand reality?)

Methodology = What tools do we use to discover knowledge? (How do we actually study something?)

Example: A healthcare researcher asks, “Are social determinants of health real factors?” (ontological question). Then: “How do we measure their impact?” (epistemological question). Finally: “Should we use surveys, medical records analysis, or interviews?” (methodological question).

Getting these straight prevents muddled research design and confused conclusions.

Ontology Powers Modern Technology: AI, Databases, and Knowledge Graphs

In information science and computing, ontology has a more concrete definition: it’s a structured representation of knowledge about a domain. An ontology explicitly defines entities, their properties, categories, and relationships.

Think of it as teaching machines what things are.

Knowledge Graphs

Search engines and AI assistants rely on knowledge graphs—massive networks of interconnected entities and relationships. These are built on explicit ontologies. Google’s knowledge graph doesn’t just store raw data; it understands that “Einstein,” “physicist,” and “1879” are related entities with specific relationship types.

Medical Ontologies

In healthcare, ontologies like SNOMED CT and MeSH standardize medical terminology. This matters because “heart attack” means something specific in databases, clinical systems, and research—preventing costly miscommunications and enabling data consistency across hospitals.

Schema.org and the Semantic Web

The semantic web uses shared ontologies so machines can interpret content meaningfully. When a website marks up structured data using Schema.org ontologies, search engines understand what that information represents.

AI Systems

Modern AI systems, especially those handling specialized domains, depend on ontologies to structure their reasoning. An AI diagnosing diseases needs an ontology that captures the relationships between symptoms, diseases, treatments, and patient factors.

Ontology Blockchain: A Real-World Application

The Ontology blockchain project (ONT) illustrates how ancient philosophical concepts power contemporary technology.

Ontology positions itself as a “trust layer” for Web3, providing decentralized identity solutions and data interoperability. The name is deliberate: just as philosophical ontology maps what exists in reality, the Ontology blockchain structures what “exists” in digital systems—identities, permissions, assets, credentials.

This reflects the deeper convergence: how we philosophically organize reality influences how we design digital infrastructure.

Building Domain Ontologies: Practical Steps

When designing systems—whether in healthcare, finance, or e-commerce—building an explicit ontology is invaluable:

1. List all relevant entities for your domain (e.g., in healthcare: diseases, symptoms, treatments, patients, providers)

2. Define relationships between entities (e.g., “treats,” “causes,” “part of,” “is a type of”)

3. Use ontology tools like Protégé (open-source editor) or OWL (Web Ontology Language) to formally represent your ontology

4. Ensure consistency so different systems interpret terms identically

This upfront work prevents messy data integration problems later. It makes your systems more intelligent, scalable, and aligned across teams.

Why Ontology Matters Now

Ontology shapes the infrastructure of modern intelligence—both human and artificial.

For researchers, clarity on ontology determines methodology and validity of conclusions. For technologists, explicit ontologies make systems more robust, interoperable, and understandable. For society, how we ontologically structure digital systems reflects and reinforces what we consider “real” and “valuable.”

As AI becomes more sophisticated and blockchain creates new digital ecosystems, ontological questions become more urgent: What does identity mean digitally? What counts as ownership in a decentralized system? How do we verify truth across distributed networks?

Understanding ontology—both the philosophical tradition and its technical applications—equips you to navigate these questions with clarity. Whether you’re designing systems, conducting research, or simply thinking critically about technology and reality, ontology provides the conceptual toolkit you need.