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Ontology

A formal, machine-readable specification of concepts and their relationships that bridges the gap between demand (Use Cases) and supply (Data Products) in the data economy

What Is an Ontology?

An Ontology is a formal specification of concepts and their relationships that provides a common language for describing business knowledge. In the context of the Enterprise Knowledge Graph, ontologies act as a crucial intermediary between the demand and supply sides of the information economy.

The Role of Ontologies in the Data Economy

Think of the data economy as having two sides:

  • Demand side — Use Cases represent the demands for information from various stakeholders. They define what data is needed and why.
  • Supply side — Data Products represent the supply of information. They define what data is available and how it can be accessed.

Ontologies bridge the gap between these two sides by providing a common language that can be used to describe the Concepts and relationships relevant to both the demand and supply sides.

Common language

Ontologies enable use cases and data products to "speak the same language," making it possible to match what's needed with what's available.

Why Ontologies Matter

Ontologies enable:

  • Semantic integration — Different systems can understand each other's data because they share the same semantic model
  • Reuse — Concepts defined once in an ontology can be reused across multiple use cases and data products
  • Consistency — Ensures that the same concept means the same thing everywhere
  • Automated reasoning — Machines can understand and reason about business concepts
  • Interoperability — Enables integration across diverse systems and technologies

This enables a more efficient and effective exchange of information, allowing organizations to better leverage their data assets and achieve their business objectives.

What Is an Ontology in the Use Case Tree Method?

An Ontology is a formal, machine-readable specification of concepts and their relationships, typically expressed using standards like OWL (Web Ontology Language) or SHACL (Shapes Constraint Language).

In the Use Case Tree Method, ontologies serve as the semantic foundation that enables the Enterprise Knowledge Graph to understand and reason about business concepts.

Ontologies as Intermediaries

Ontologies act as a crucial intermediary between the demand and supply sides of the information economy:

  • Demand side — Use Cases represent the demands for information from various stakeholders. They define what data is needed and why.
  • Supply side — Data Products represent the supply of information. They define what data is available and how it can be accessed.

The ontologies help bridge the gap between these two sides by providing a common language that can be used to describe the concepts and relationships relevant to both the demand and supply sides.

How Ontologies Bridge the Gap

Ontologies enable semantic matching between use cases and data products:

  1. Use Cases define concepts — Each use case has a vocabulary of Concepts that represent the domain knowledge it needs
  2. Concepts link to ontologies — Concepts are linked to ontology classes, properties, and shapes that define their semantic meaning
  3. Data Products conform to ontologies — Data products specify which ontologies their datasets conform to
  4. Semantic matching — The EKG can match use cases to data products based on shared ontology concepts

This semantic matching enables the EKG to automatically discover which data products can fulfill the requirements of a given use case.

Ontology Standards

Ontologies in the Use Case Tree Method typically use:

  • OWL (Web Ontology Language) — For defining classes, properties, and relationships with rich logical expressivity
  • SHACL (Shapes Constraint Language) — For defining validation constraints and data shapes
  • RDFS (RDF Schema) — For basic class and property hierarchies

These standards enable:

  • Machine readability — Ontologies can be processed by automated systems
  • Reasoning — Automated reasoners can infer new knowledge from ontology definitions
  • Validation — Data can be validated against ontology constraints
  • Interoperability — Different systems can share and reuse ontology definitions

Relationship to Concepts

Concepts serve as the linking pin between business language and ontologies:

  • Business language — Concepts capture the terms and ideas that the business uses
  • Ontology mapping — Concepts link to ontology classes, properties, and shapes that define their semantic meaning
  • Multiple mappings — A single concept can map to multiple ontologies, enabling integration across different standards

This dual nature allows the EKG to address "the business" with their language while maintaining formal semantic models that enable automated reasoning and integration.

Reuse and Standardization

Ontologies enable reuse and standardization:

  • Standard ontologies — Organizations can use existing standard ontologies (e.g., FIBO, CDM, schema.org) rather than creating everything from scratch
  • Domain ontologies — Domain-specific ontologies can be created and reused across use cases in the same domain
  • Extension — Standard ontologies can be extended to meet specific organizational needs
  • Composition — Multiple ontologies can be combined to create comprehensive semantic models

This reuse ensures consistency across the enterprise and enables interoperability with external systems and standards.

Relationship to Other Concepts

Ontologies relate to other core concepts:

  • Concepts — Concepts link to ontology classes, properties, and shapes to define their semantic meaning
  • Use Cases — Use cases have vocabularies of concepts that link to ontologies
  • Data Products — Data products specify which ontologies their datasets conform to
  • Personas — Personas are Concepts that link to ontology classes, enabling semantic definition

This integration ensures that ontologies are not isolated specifications but part of a cohesive, semantic model of the enterprise.