Concepts
Classes describe categories of entities, such as a cell, a disease, a chemical substance or a biological process.
SCIENTIFIC KNOWLEDGE / ONTOLOGY
Ontology.
The structure of
meaning.
KNOWLEDGE REPRESENTATION / 001
Discover how ontologies define concepts and relationships, connect scientific information, and provide a foundation for semantic technologies, biomedical research and AI.
THE SCIENCE OF STRUCTURED KNOWLEDGE
Information describes.
Information describes.
Ontology
defines.
Scientific disciplines produce an enormous variety of observations, measurements and terminology. An ontology helps establish what concepts mean, which relationships can hold between them, and how knowledge can be interpreted consistently across systems.
CONCEPTS / RELATIONSHIPS / LOGIC
MODEL 001
CONCEPTUAL EXAMPLE / NOT A FORMAL IMPORTED ONTOLOGY
002 / FOUNDATIONS
What is an ontology?
In information science, an ontology is a structured, explicit representation of knowledge about a particular domain. It describes the kinds of entities being considered, the relationships among them and, in formal ontologies, logical constraints that govern those relationships.
Ontologies provide a shared interpretation of terminology. For example, the term "cell" has a specific biological meaning. A biomedical ontology can distinguish a cell from a tissue, describe classes of cells and represent relationships involving biological processes.
The word ontology also has a longer history in philosophy, where it concerns the nature of being and existence. In computer science and bioinformatics, the term generally refers to a computational model of concepts and their relations.
An ontology is more than a collection of keywords. It makes meaning explicit, helping humans and software distinguish concepts that may otherwise be ambiguous or inconsistently described.
003 / ONTOLOGY ANATOMY
Four building blocks of structured meaning.
Formal knowledge models use several complementary elements to describe their domains with clarity and precision.
Properties
Relationships describe how entities connect, including subclass relations, part-whole relations and other domain-specific associations.
Instances
Instances represent specific entities, such as an individual sample or a particular experimental observation, when appropriate to the ontology model.
Logical Rules
Axioms express formal constraints and definitions that allow reasoning systems to check consistency and derive logical consequences.
004 / EXAMPLE IN BIOLOGY
From biological terms to scientific relationships.
Consider the relationship between cells and their specialized types. In everyday language, scientists may describe neurons and macrophages as cells. A simple ontology model can express that relationship explicitly by defining both as subclasses of the class Cell.
This representation helps computational systems understand that a neuron belongs to a broader category. If additional formal axioms are defined, reasoning systems may derive further classifications.
The illustrated example is intentionally simplified. Production biomedical ontologies require precise identifiers, definitions, relationships and expert review.
MODEL 002 / SIMPLIFIED BIOLOGICAL CLASSIFICATION
Illustrative class hierarchy for education, not a reproduction of the formal Cell Ontology.
005 / IMPORTANT DISTINCTIONS
Ontology, taxonomy, vocabulary and knowledge graph.
Related knowledge-organization technologies serve different purposes and may be combined in one system.
| Concept | Primary Role | Illustrative Example |
|---|---|---|
| Controlled vocabulary | Uses consistent terms to describe information. | Standardized labels for research samples. |
| Taxonomy | Organizes concepts into hierarchical groups. | Classification of types of laboratory materials. |
| Ontology | Defines concepts, relationships and potentially formal logical constraints. | Model of cell types and biological functions. |
| Knowledge graph | Represents connected entities and relationships, potentially using ontologies. | Network connecting genes, pathways and diseases. |
006 / THE ONTOLOGY KNOWLEDGE HUB
Explore the five dimensions of ontology.
Move from foundational knowledge to specialized scientific applications. Each topic has its own dedicated page for deeper explanations, technical examples and research context.
01 /
Semantic Technologies
Understand the Semantic Web, RDF, OWL, SPARQL, linked data and how technologies can represent and query meaning.
02 /Biomedical Ontologies
Explore Gene Ontology, phenotype ontologies, cell classification, chemical entities and biomedical data integration.
03 /Ontology Engineering
Learn how ontologies are designed, evaluated, maintained and documented, from conceptual modeling to logical validation.
04 /Ontology Applications
Discover applications in scientific databases, biotechnology, interoperability, research knowledge graphs and structured information.
05 /Ontology & AI
Investigate the connections between structured knowledge, symbolic reasoning, machine learning, neuro-symbolic AI and retrieval systems.
007 / SCIENTIFIC REFERENCE SYSTEMS
Ontologies at work in real science.
These established community resources demonstrate how structured knowledge supports biology, biotechnology, medicine and scientific data interoperability.
Gene Ontology (GO)
Provides standardized descriptions of biological processes, molecular functions and cellular components associated with gene products.
Visit Gene Ontology ↗Human Phenotype Ontology (HPO)
Provides a structured vocabulary for phenotypic abnormalities encountered in human disease, supporting research and phenotype-based computational analysis.
Visit HPO ↗ChEBI
Chemical Entities of Biological Interest describes small molecular entities and their relationships in biological and chemical contexts.
Visit ChEBI ↗OBO Foundry
Coordinates principles and community practices for interoperable biological and biomedical ontologies, including openness, definitions and shared relations.
Visit OBO Foundry ↗W3C Web Ontology Language
OWL is a formal ontology language for representing classes, properties and logical relationships, supporting automated interpretation and reasoning.
Explore OWL 2 ↗
008 / FREQUENT QUESTIONS
Ontology, explained.
What is ontology in simple terms?
An ontology is a structured representation of concepts and their relationships. It gives information a consistent meaning that people and computer systems can share.
What is an example of ontology in biology?
Gene Ontology is an established example. It provides controlled, structured descriptions of functions, processes and cellular components associated with genes and gene products.
What is the difference between an ontology and a knowledge graph?
An ontology defines concepts and relationships and may include logical constraints. A knowledge graph represents connections between entities. A knowledge graph can use an ontology to give those connections consistent semantics.
Why is ontology useful in artificial intelligence?
Ontologies can provide explicit domain knowledge, consistent terminology and logical constraints. They may support reasoning, information integration and the interpretation of data used by AI systems.
Do all ontologies use OWL?
No. OWL is a widely used formal ontology language, but ontologies and terminology systems may use different representation formats depending on their requirements and logical expressiveness.
ONTOLOGY ONLINE / SCIENTIFIC KNOWLEDGE
Better structure.
Explore Semantic Technologies ↗
Better structure.
Deeper understanding.
Explore how scientific terminology, computational semantics and formal knowledge models connect information across modern research.