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About 15 years ago I was asked about my thoughts on taxonomies and ontologies, my mind glazed, my stomach dropped, what the what? was I meant to be clever enough to know what these were? I could barely spell the words let alone give any thought or opinion. I’ve just been reminded of them when chatGPT mentioned the dreaded taxonomy word. If only I had chatGPT back then, I might have got away with not listening in school.

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Taxonomies and ontologies are classification systems used in various fields like biology, information science, and artificial intelligence to organise knowledge. They have some overlap but are distinct in structure and purpose.

Taxonomies

A taxonomy is a hierarchical system of classification that organizes entities into categories and subcategories, often based on shared characteristics. It represents a tree-like structure, with more general categories at the top and more specific subcategories branching out below.

• Example in biology: The classification of living organisms, where Kingdom is the highest category (e.g., Animalia), followed by Phylum, Class, Order, Family, Genus, and Species.
• Structure: Linear and hierarchical.
• Purpose: Primarily used for categorization and classification of things, typically focusing on “what is this entity?”

Ontologies

An ontology, on the other hand, is a more complex and comprehensive framework. It not only organizes entities into categories but also defines the relationships between them, including properties and interactions. Ontologies often include non-hierarchical relationships and are more flexible in defining knowledge.

• Example in computer science: The Semantic Web, where an ontology defines the relationships between various web entities, like “a Person owns a Car,” “a Car has a Manufacturer,” etc.
• Structure: Network-like with entities, attributes, and relationships.
• Purpose: Describes not just the classification but also the attributes and relationships of entities. It seeks to answer not just “what is this entity?” but also “how is this entity related to others?”

Key Differences:

• Structure: Taxonomies are hierarchical, while ontologies are more networked and flexible.
• Focus: Taxonomies focus on categorization, whereas ontologies focus on understanding relationships and interactions.

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How are they used?

Taxonomies and ontologies are used across various fields to structure information and knowledge. Here’s how they are applied in different domains:

1. Information Science and Data Management

Taxonomies and ontologies are widely used in information retrieval systems, digital libraries, and knowledge management to organize data, facilitate searching, and ensure information consistency.

• Taxonomies: Used to categorize and tag content for easy retrieval. For example, a company may have a taxonomy to classify documents into categories like “HR policies,” “Financial reports,” and “Marketing materials.”
• Ontologies: In information systems, ontologies are used to model complex relationships between data entities. They enable more advanced queries and reasoning. For instance, in a customer relationship management (CRM) system, an ontology might represent relationships like “Customer X purchased Product Y” or “Customer Z is interested in Category A products.”

Example: The Library of Congress Classification System uses a taxonomy to categorize books by subject, while ontologies in knowledge graphs (e.g., Google Knowledge Graph) help connect concepts (e.g., “Einstein” is related to “Physics,” “Relativity Theory,” and “Nobel Prize”).

2. Biology

In biology, taxonomies are foundational for organizing living organisms, while ontologies are crucial for understanding the relationships between biological entities.

• Taxonomies: The most famous example is the Linnaean taxonomy, which classifies all living organisms into a hierarchical structure of Kingdom, Phylum, Class, Order, Family, Genus, and Species. This helps scientists to group organisms based on evolutionary relationships.
• Ontologies: In biology, ontologies like the Gene Ontology (GO) are used to describe the relationships between genes, proteins, and biological processes. GO defines concepts like “gene X is involved in metabolic process Y” or “protein A interacts with protein B.”

Example: GO helps biologists understand complex molecular functions, biological processes, and cellular components by linking genes to their roles in these processes, thus supporting systems biology and functional genomics.

3. Artificial Intelligence and the Semantic Web

In AI, taxonomies and ontologies help machines understand, process, and reason about information. The Semantic Webrelies heavily on ontologies to define relationships between web resources.

• Taxonomies: Used in simpler systems like recommendation engines, where products or content are classified into categories, making it easier for algorithms to suggest relevant items.
• Ontologies: Ontologies define the relationships between entities on the web, enabling more intelligent reasoning by machines. For example, if a webpage talks about a “book,” an ontology might tell the system that the “author” is a person, the “publisher” is an organization, and “published date” is a time event.

Example: The OWL (Web Ontology Language) is a framework used to create complex ontologies for the Semantic Web. It helps define relationships like “a doctor treats patients,” “patients can visit hospitals,” and “hospitals have medical departments.”

4. Healthcare

In healthcare, taxonomies and ontologies play an essential role in structuring medical knowledge for clinical decision support systems, research, and patient care.

• Taxonomies: Used to categorize diseases, symptoms, and treatments. The ICD (International Classification of Diseases) is a well-known medical taxonomy that provides a hierarchical classification of diseases and health conditions.
• Ontologies: In healthcare, ontologies like SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) define medical concepts and relationships between them, such as “diabetes mellitus is a type of metabolic disorder” or “aspirin is a treatment for inflammation.”

Example: Ontologies help in clinical decision-making systems by modeling relationships like “a patient with condition X should not take medication Y,” facilitating better, personalized healthcare recommendations.

5. E-commerce

In e-commerce, taxonomies and ontologies are used to categorize products, personalize shopping experiences, and improve search functionality.

• Taxonomies: Used to organize products into categories and subcategories. For instance, an online store might categorize products as “Electronics > Mobile Phones > Smartphones” or “Clothing > Men’s > T-Shirts.”
• Ontologies: Used to understand product attributes and customer preferences. For example, an ontology might define relationships like “Smartphone A has 5G connectivity,” “Customer B prefers brands X and Y,” and “Product C is compatible with Device D.”

Example: An ontology-based system can improve product recommendations by understanding not only what a product is (via taxonomy) but also how it relates to a customer’s preferences (via ontology).

6. Business Process Management

In business, taxonomies and ontologies help structure processes, roles, and workflows, making systems more efficient and facilitating communication between different departments.

• Taxonomies: Used to classify business documents, roles, or processes. For example, a company might have a taxonomy for different departments (HR, Finance, Marketing) or document types (contracts, invoices, reports).
• Ontologies: Can model relationships between roles, activities, and resources in a company. For instance, an ontology might define that “Manager A approves Expense Reports” or “Department B is responsible for Marketing Campaigns.”

Example: BPMN (Business Process Model and Notation) is often used to define workflows, and an ontology can add additional layers of meaning, such as how departments, employees, and software systems interact during these workflows.

7. Education and Learning Systems

In education, taxonomies and ontologies help structure curriculum, educational resources, and learning pathways.

• Taxonomies: Used to classify educational content or skills. For example, Bloom’s Taxonomy organizes learning objectives into hierarchical levels such as remembering, understanding, applying, analyzing, evaluating, and creating.
• Ontologies: Help define learning paths, relationships between knowledge areas, and how different skills interrelate. For example, an ontology in an e-learning system might define that “learning Algebra is prerequisite to Calculus” or “Programming knowledge includes understanding data structures and algorithms.”

Example: Ontologies enable personalized learning by suggesting courses based on what the student has already learned, their skill levels, and their career goals.

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In summary, taxonomies and ontologies are powerful tools for structuring knowledge and facilitating both human and machine understanding. Taxonomies organise data hierarchically, making it easier to find and classify information, while ontologies provide deeper insights into the relationships and attributes of data, enabling more complex reasoning and decision-making.

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