Master of Science in Computational Ontology & AI
The Master of Science in Computational Ontology & AI provides a specialized exploration into the integration of ontological structures with artificial intelligence, enhancing knowledge representation and semantic reasoning. This program is designed for students seeking to merge the rigorous frameworks of ontology engineering with the dynamic capabilities of AI-driven logic systems, knowledge graphs, and automated reasoning.
About the Program
The Master of Science in Computational Ontology & AI offers a cutting-edge curriculum that prepares students to design and implement intelligent data systems and decision-support models. This program focuses on the practical applications of ontological principles in AI, including machine learning, intelligent automation, and AI-based semantic networks.
Students will gain hands-on experience with AI-driven logic systems, developing skills in knowledge graph construction and automated reasoning. The coursework encourages a deep understanding of how ontological modeling can enhance the functionality and efficiency of AI systems across various industries.
Key Areas of Study
Ontology Engineering and Management
AI-Driven Logic Systems and Knowledge Representation
Knowledge Graphs and Semantic Networks
Automated Reasoning and Intelligent Automation
Machine Learning Architectures and Their Ontological Foundations
Who Should Enroll?
This program is ideally suited for professionals and researchers interested in AI knowledge engineering, ontological modeling, intelligent systems development, and machine learning architecture. Graduates will be well-prepared to lead advancements in AI technologies, leveraging ontological insights to build more sophisticated and effective systems.
Core Curriculum & Program Structure
Program Courses: 57 credits
Degree Requirements
Total Credits Required: 57 credits
Core Major Courses: 39 credits
Research & Thesis: 12 credits
Electives: 6 credits
Year OneOne – Advanced Ontological AI & Knowledge Systems
Falll Semester 1
COA 601 – Advanced Ontology & AI Knowledge Representation (3 credits)
COA 602 – Description Logics & Semantic Reasoning (3 credits)
COA 603 – AI-Driven Ontologies & Knowledge Graphs (3 credits)
COA 604 – Machine Learning for Ontology Evolution (3 credits)
Research Methods in Computational Ontology & AI (3 credits)
Spring Semester 2
COA 605 – Automated Theorem Proving & Ontological AI (3 credits)
COA 606 – AI Ethics & Ontological Frameworks in Decision Systems (3 credits)
COA 607 – Probabilistic Reasoning & Epistemic AI (3 credits)
COA 608 – Computational Linguistics & Ontological Semantics (3 credits)
Research Project in AI & Knowledge Engineering (3 credits)
Year OneTwo – Specialized Research & AI System Integration
Falll Semester 3
COA 701 – Cognitive AI & Ontological Decision Systems (3 credits)
COA 702 – Large-Scale Knowledge Systems & AI Optimization (3 credits)
COA 703 – Human-Centered AI & Ontology-Driven Interfaces (3 credits)
Elective in AI, Machine Learning, or Knowledge Engineering (3 credits)
Independent Research in Computational Ontology (3 credits)
Spring Semester 4
COA 704 – Capstone Thesis in Computational Ontology & AI (6 credits)
COA 705 – Experimental Research in AI Reasoning & Knowledge Systems (3 credits)
Final Research Elective or Internship in Ontological AI (3 credits)