Ontology Structure

Ontologies are typically modular — designed to be extended and reused across domains. This modularity ensures flexibility, scalability, and interoperability, allowing ontologies to be tailored for specific domains and applications while remaining consistent with broader frameworks.

The Benefits of Modularity

Scalability: Modular ontologies can grow incrementally by adding new modules without overhauling the entire system.

Reusability: Common frameworks and domain ontologies can be reused across multiple applications, reducing duplication of effort.

Interoperability: Modular design ensures compatibility between different ontologies, enabling seamless data integration and exchange.

The modular nature of ontologies reflects their purpose: to provide a structured yet flexible way to represent and share knowledge, making them indispensable tools for modern information systems.

Vocabularies, Taxonomies, and Ontologies

Vocabularies A vocabulary is a list of agreed terms and their definitions. It ensures consistency in naming — for example, always using “electrode” instead of “electrode material” or “electrochemical interface." Vocabularies standardize terminology but do not describe how terms are related.

Taxonomies A taxonomy organizes vocabulary terms into a hierarchy of classes and subclasses, typically using “is a” relationships. For instance:

Animal
  └── Mammal
       └── Human
            └── Scientist

Taxonomies capture classification and inheritance but not deeper meanings such as properties, processes, or rules.

Ontologies An ontology extends beyond vocabularies and taxonomies. It defines not only terms and hierarchies, but also relationships, properties, and logical rules that describe how entities interact. For example, an ontology can express that:

"Every electrode has a material,"
"Every electrochemical reaction involves at least one electrode,"
"Graphite is a kind of carbon material used in electrodes."

This allows reasoning and querying, which are capabilities that vocabularies and taxonomies alone cannot provide.

Tip

You can think of these as layers of complexity: Vocabulary → Taxonomy → Ontology.

Top-Level Ontologies

At the source of many ontologies are top-level frameworks, often called upper ontologies. These define the most general concepts and relationships that apply across multiple domains. Top-level ontology frameworks establish the rules and shared vocabulary for creating domain-specific ontologies. They provide the "grammar" for more specialized knowledge representations and promote the re-use of existing concepts within their internal systems.

Tip

Some Common Top-Level Ontologies

EMMO is specialized ontology developed for materials science and engineering. It integrates perspectives from physics, chemistry, and engineering to create a cohesive framework for modeling materials and their properties. It supports data interoperability and advanced simulations across disciplines.

BFO is a widely used top-level ontology in science and healthcare. It provides a framework for domain-specific extensions by focusing on general concepts such as objects, processes, and their interrelations. It emphasizes simplicity and consistency, making it a reliable backbone for more specialized ontologies.

DOLCE is designed to represent common human experiences and conceptualizations. It captures everyday knowledge and cognitive structures. Its focus on common sense reasoning makes it particularly valuable for applications in linguistics, cognitive science, and AI.

Domain Ontologies

Domain ontologies take the foundational concepts provided by top-level frameworks and expand them to cover knowledge specific to particular fields. For instance, the Gene Ontology (GO) delves into the intricacies of biological processes, cellular components, and molecular functions, offering a structured way to describe the complexity of life at a molecular level. By building on the shared principles of top-level ontologies, domain ontologies create a consistent and interoperable framework, enabling seamless integration of data across diverse fields.

Application Ontologies

Application ontologies extend general and domain-specific knowledge to address specialized needs. For example, a healthcare ontology might focus on patient management by detailing intake processes, diagnostic tools, and treatment plans, tailoring it to hospital operations. These extensions achieve a balance between addressing unique needs and staying compatible with broader frameworks.

Knowledge Graphs

A knowledge graph is a practical implementation of an ontology that links specific pieces of data—individual entities and their relationships—into a connected network. While ontologies define general concepts and how they relate, knowledge graphs represent the actual instances of those concepts, forming a rich, navigable web of information.

Built on ontologies, knowledge graphs enable data integration, discovery, and reasoning across diverse sources. They power modern search engines, recommendation systems, and digital assistants, delivering context-aware results that reflect human-like understanding of information.