An Introduction to Ontology Engineering

Build knowledge graphs that work. Ontology engineering sits at the intersection of philosophy, logic, and computing. This rigorous, practical guide shows you how to turn real requirements into precise, shared models that power interoperability, data integration, and intelligent services.

An Introduction to Ontology Engineering blends foundations with practice-so you learn why things are modeled a certain way and how to implement them using today’s standards and tools.

What you’ll learn

Logical foundations: first-order and description logics, reasoning services, and OWL 2 profiles (EL, QL, RL).
Languages and standards: RDF/RDFS, OWL 2, SHACL, JSON-LD, Manchester & Functional Syntax.
Tools and platforms: Protégé, reasoners (HermiT, ELK), triple stores, SHACL validators, CI pipelines.
Methodology: requirements and competency questions, ORSD, test-driven ontology development.
Foundational ontologies: BFO, DOLCE, UFO-alignment strategies and bridging modules.
Bottom-up construction: reuse from data, text, and legacy assets; R2RML/RML mappings.
OBDA & querying: QL-friendly modeling, SPARQL rewriting, virtual graphs over relational sources.
Language layer: SKOS labels/definitions, OntoLex-Lemon lexica, multilingual UX.
Advanced modeling: time, change, uncertainty, context, modality (OWL-Time, PROV, policy shapes).
Modularity & evolution: versioning, deprecation, diffs, release engineering.
Evaluation & QA: consistency/coherence checks, SHACL suites, golden queries, performance gates.
Interoperability: alignment, mapping, merging, reconciliation-with provenance and confidence.
Ethics & governance: privacy by design, consent/purpose scoping, retention, sustainability.

Inside the book

A running “University Admissions” example-from competency questions to OWL axioms and SHACL shapes.
Ready-to-adapt snippets in Turtle, Manchester syntax, and SHACL.
Checklists, patterns, and test templates you can drop into projects.

Who it’s for

Advanced undergraduates and graduate students in AI, data science, information systems, and knowledge representation.
Engineers and practitioners building knowledge graphs, semantic integration, or ontology-driven apps.

Key outcomes

Choose the weakest logic that meets your needs-then model confidently.
Combine OWL for meaning with SHACL for validation under real-world constraints.
Ship ontologies with tests, releases, and governance your stakeholders can trust.