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Ontology
Overview
An Ontology defines a structured schema for understanding data. It allows you to introduce predefined types, attributes, and relationships that guide how the knowledge graph is built and interpreted.
By using ontologies, Praxos can capture and organize data more accurately, leveraging domain-specific structure rather than relying solely on implicit patterns in a source.
Why Use Ontologies?
Ontologies provide a way to shape how data is interpreted by:
- Defining entities, attributes, and relationships ahead of time
- Enabling consistent and predictable graph structures
- Improving extraction accuracy by using user-defined expectations
- Supporting richer and more reliable querying
They are especially useful when dealing with structured business data or well-known domains such as finance, healthcare, or customer service.
Scope and Behavior
- Ontologies are applied at the environment level.
- Each environment can support multiple ontologies.
- When an ontology is applied to an environment, it automatically influences how all sources within that environment are processed.
- This means that entity recognition, relationship extraction, and graph construction are all guided by the ontology’s definitions.
Structure
| Field | Type | Description |
|---|---|---|
name | string | Unique name of the ontology (required) |
description | string | Optional description providing context or purpose |
Ontology names must be unique across all of ontologies. Attempting to create another ontology with the same name will result in an error.
Summary
- Ontologies act as blueprints for structured knowledge.
- They enhance accuracy and control over how the knowledge graph is built.
- Applied at the environment level, they affect all sources in that environment.
- Multiple ontologies can coexist within a single environment, enabling modular and flexible schema design.