Ontology and Artificial Intelligence

Course Overview

• Course Type: General Education (Online Theory)
• Credits: 3 credits / 3 hours per week
• Program: AI Aerospace (항공우주·미래모빌리티 융합전공)
• Recommended Year: All years

Course Description

This course covers the fusion of knowledge representation (ontology) and AI technology. The core philosophy centers on Ontology-First Thinking and Decision Science, shifting from data-centric to decision-centric AI understanding with emphasis on structural thinking. Students critically analyze generative AI and LLMs while practicing semantic ontology modeling.

The AI Aerospace specialization track emphasizes ontology applications in aerospace and future mobility — aircraft-mission-environment structuring, satellite-sensor-data relationships, aviation information reference models, and autonomous system design.

Learning Objectives

1. Understand ontology concepts and knowledge representation fundamentals
2. Distinguish between Data Science and Decision Science paradigms
3. Build semantic models and knowledge graphs for domain-specific applications
4. Critically evaluate generative AI and LLM capabilities and limitations
5. Design autonomous decision systems using ontology-based reasoning
6. Apply ontology modeling to aerospace domains (UAM, drones, satellite systems)

Course Outline

Unit 1 — Foundations (Weeks 1–4)

• Week 1: Ontology concepts and necessity — why structure matters before data
• Week 2: Data Science vs. Decision Science paradigm shift
• Week 3: Knowledge representation methods and semantic web basics
• Week 4: Introduction to ontology modeling tools and languages (OWL, RDF)

Unit 2 — AI and Knowledge Systems (Weeks 5–8)

• Week 5: Generative AI and LLM architecture — critical evaluation
• Week 6: Knowledge graphs — construction, querying, reasoning
• Week 7: Ontology-first design patterns for AI systems
• Week 8: Midterm — Socratic dialogue-based QA via AI platform

Unit 3 — Aerospace Domain Applications (Weeks 9–12)

• Week 9: Aircraft/mission/environment/regulation ontology modeling
• Week 10: Satellite-sensor-data semantic connections (SOSA/SSN ontology)
• Week 11: Aviation information reference models (AIRM, FIXM, K-UAM standards)
• Week 12: Autonomous flight, air traffic management, satellite image analysis

Unit 4 — Integration and Ethics (Weeks 13–15)

• Week 13: Autonomous decision systems design — integrating ontology and AI
• Week 14: AI ethics and governance in aerospace applications
• Week 15: Final Exam — essay report (choose 1 of 3: AI Service Plan, Domain Ontology Modeling Report, or Ethical AI Guidelines Proposal)

Evaluation

Key Topics

• Ontology-First Thinking
• Decision Science vs. Data Science
• Semantic Web (OWL, RDF, SPARQL)
• Knowledge Graphs and Reasoning
• Generative AI / LLM Critical Analysis
• Aerospace Ontology (UAM, Satellite, Aviation Standards)
• AI Ethics and Governance