What is Ontology?
An ontology is a formal, structured model that defines the key concepts in a domain and the relationships between them, allowing data to be organized and interpreted with explicit meaning.
It represents knowledge using classes (categories of things), properties (attributes or relations), and instances (specific examples), often expressed in standardized languages like OWL or RDF.
Ontologies enable machines to perform logical reasoning, infer new facts, and integrate data from different sources by making implicit assumptions explicit and machine-readable.
Unlike simple lists or taxonomies, ontologies support complex relationships, constraints, and rules that capture real-world semantics beyond hierarchical structures.
Example
A medical ontology might define 'HeartDisease' as a subclass of 'CardiovascularCondition' that has properties like 'hasSymptom' linking to 'ChestPain' and 'treatedBy' linking to 'Medication'.
Why it matters
Ontologies power knowledge graphs, semantic search, and interoperable AI systems by giving data explicit meaning, improving accuracy in applications like recommendation engines and intelligent assistants.
Frequently asked questions
A database schema focuses on data storage and structure, while an ontology emphasizes meaning, relationships, and reasoning across domains.
Related terms
A knowledge graph is a structured data model that represents real-world information as a network of entities (nodes) connected by relationships (edges). It organizes facts in a machine-readable way to support querying, reasoning, and integration across sources.
Batch size is the number of training examples processed together in a single forward and backward pass during model training.
Chunking is the process of breaking large datasets, documents, or files into smaller, fixed-size or semantically meaningful segments. It is a common data preprocessing step in AI/ML pipelines to manage memory and enable efficient processing.
Cosine similarity measures how similar two vectors are by computing the cosine of the angle between them, ignoring their magnitudes.
Data augmentation is a technique that artificially increases the size and diversity of a training dataset by creating modified versions of existing data samples.
Data labeling is the process of adding tags or annotations to raw data so that machine learning models can learn from it during training.