# Introduction to Ontology Concepts and Modeling

Discover the power of modeling conceptual relationships

- **Source:** [Boxes and Arrows](https://brifing.si/source/ed5d6920-da14-4f71-859a-996c36b97846)
- **Published:** 2021-11-02
- **Topic:** [Data & AI](https://brifing.si/c/data-ai)
- **Original article:** <https://boxesandarrows.com/introduction-to-ontology-concepts-and-modeling/>
- **On Brifing:** https://brifing.si/s/f1e0a2af-859b-4f80-aff3-26aeb2ecf9a1

## What is Ontology?

An ontology is a formal system for modeling concepts and their relationships. Unlike relational databases, ontologies store data in graph structures emphasizing relationship importance. This approach excels particularly in expressing complex, multi-faceted data connections.

## Limitations of Relational Databases

While relational databases handle one-to-one relationships well, they struggle with real-world complex relationships. These systems require additional tables and key-value pairs, where connections between data elements are implied by column headers rather than explicitly stated.

## Graph-Based Approach

In ontologies, data is structured as nodes (objects) and edges (relationships). This graph model allows relationships to be directly represented as system objects. Each node can be expanded to reveal new connections, facilitating data discovery.

## Ontological Triples

The RDF (Resource Description Framework) standard stores ontological data as subject-predicate-object triples. This simple structure models real-world information parallel to human understanding. Each triple explicitly defines specific relationships between two objects.

## Upper and Lower Ontologies

Ontology systems consist of two parts: Upper ontology defines classes of objects and permitted relationships as schema. Lower ontology contains concrete data populating this schema. Upper ontologies maintain consistency across different datasets.

## SKOS and Taxonomies

SKOS is an RDF-compliant ontology model designed to structure and share taxonomies. While all taxonomies can be expressed as ontologies, ontologies allow more complex relationship types beyond taxonomic hierarchies.

## Advantages of Ontologies

Ontologies are extensible, shareable, and interoperable. Their machine-readable structure and inference capabilities play crucial roles in AI applications. Being standards-based enables their use across different systems.

## Why Use Ontology?

Ontologies become indispensable when object relationships are critical in datasets. For modeling complex information networks, data integration, and machine learning applications, ontological approaches offer clear advantages over traditional methods.

## Sources covering this story

- https://boxesandarrows.com/introduction-to-ontology-concepts-and-modeling/ — 

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_Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._