jnaapti

AI curriculum

Course outline for Semantic Web and Ontology Engineering

Integrating knowledge requires shared meanings as well as shared formats. Build a small ontology and query its facts while making reasoning assumptions explicit.

Technology
Knowledge Engineering
Duration
6 hours
Modules
3 · 2 hours each
Level
Prerequisites listed

About Semantic Web and Ontology Engineering

Integrating knowledge requires shared meanings as well as shared formats. Build a small ontology and query its facts while making reasoning assumptions explicit.

Semantic Web and Ontology Engineering Course Objectives

  • Represent domain facts using RDF vocabulary.
  • Write SPARQL queries over a small dataset.
  • Explain ontology inference and open-world limitations.

Pre-requisites

  • Familiarity with structured data and relationships.
  • Basic query or programming experience.

Lab Setup

  • Computer with a local ontology editor and RDF query tooling.
  • Use small public or synthetic datasets; no hosted graph service is required.

Detailed Course Outline

Proposed modules

Module 1: Semantic representations

  • RDF
  • RDFS
  • OWL

Practical outcome: Encode facts and classes in a small semantic model.

Module 2: Querying and modelling

  • SPARQL
  • Ontology modelling
  • Description logic

Practical outcome: Query relationships and express simple ontology constraints.

Module 3: Reasoning assumptions

  • Open-world assumption
  • Reasoning
  • Knowledge integration

Practical outcome: Explain inferred facts and unresolved information.

Practical exercise

  • Build and query a small ontology that combines two sample datasets and document its assumptions.

How we train

Contact us for full course details, including duration, delivery options and lab requirements.

Ask about exercises, instructor feedback and the prior knowledge you need. Tool-specific courses marked provisional may become modules in a broader course.

Discuss your learning goals