Over the past few months, I have been exploring a topic that has changed the way I think about software, data, and complex systems: ontology engineering.
My journey started with theory.
I began reading C. Maria Keet's An Introduction to Ontology Engineering, and the book helped me build a much stronger foundation for understanding what an ontology actually is, how concepts and relationships can be formally represented, and how ontology engineering connects to practical software systems.
But the more I read, the more I wanted to move beyond theory.
I started asking a simple question:
What happens when ontology engineering meets real-world software?
That question led me into several fascinating areas.
I began exploring Ontology-Based Data Access (OBDA) and the idea of using an ontology as a semantic layer between applications and underlying data sources.
Instead of applications only thinking in terms of tables, columns, and database schemas, an ontology can provide a higher-level representation of concepts and relationships.
I also started exploring knowledge graphs and how connected entities can provide context that isolated data often cannot.
From there, my curiosity moved toward another challenge: decision-making.
How can a system use ontology-based knowledge to understand a situation?
How can relationships between entities provide context for a decision?
And how can language contribute to that process?
This led me to explore the idea of interpreting decision phrases and lexical expressions and connecting them to concepts represented in an ontology.
A phrase can contain intent, context, entities, relationships, and domain-specific terminology.
The challenge becomes connecting human language with structured knowledge.
I also became increasingly interested in how ontology-driven approaches are used in modern systems involving AI, knowledge representation, complex operational environments, and large-scale data integration.
I have been particularly interested in publicly available examples of ontology-driven platforms such as Palantir and the broader challenge of creating systems that can connect people, events, organizations, assets, and information into a meaningful operational model.
This exploration eventually became practical.
At my company, I have been working on building an ontology engine with a focus on resilience and performance.
That engineering work has also given me an opportunity to explore the strengths of both Go (Rust for incoming usage since im still beginner in Rust ;) ).
Go has helped with building pragmatic, concurrent, and resilient backend components.
Rust has helped me think more deeply about performance, correctness, memory safety, and strongly structured domain models.
Together, they have made me increasingly interested in a larger engineering question:
How can we build fast, resilient systems that don't just store information—but understand the relationships and meaning behind it?
This is the beginning of a series where I will share what I am learning and building.
I'll explore ontology engineering from both perspectives:
theory and practice.
Topics will include:
- Ontology engineering fundamentals
- C. Maria Keet's work and the foundations that helped me get started
- Ontology-Based Data Access
- Knowledge graphs
- AI and knowledge representation
- Ontology-driven operational systems
- Lexical interpretation and decision phrases
- Decision-making and contextual reasoning
- Building resilient ontology engines
- Go and Rust for high-performance ontology systems
I'm not writing this series because I believe I have all the answers.
I'm writing it because I believe the best way to understand a complex subject is to study it, build with it, question it, and share what you learn.
This is the beginning of my journey into ontology engineering.
And I'm excited to explore where theory meets production software.
This post was handwritten and enhanced with AI, but the entire content was written by me. :)
Reference
C. Maria Keet — An Introduction to Ontology Engineering
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