Bachelor of Science in Computational Ontology & AI (B.S.C.O.A.I.)
About the Program
The Bachelor of Science in Computational Ontology & AI provides a comprehensive exploration of the intersection of artificial intelligence, knowledge representation, and formal ontology. This program is designed for students who are keen to develop expertise in semantic modeling, intelligent reasoning, and AI-driven knowledge graphs that enhance machine learning, decision support systems, and automated reasoning capabilities.Program Overview
The B.S. in Computational Ontology & AI equips students with the skills to create and manage advanced knowledge architectures that underpin various AI systems. By integrating formal ontology with computational logic, AI ethics, and intelligent data systems, the program offers a robust foundation in designing and implementing sophisticated semantic web technologies and ontological structures. Students will engage with cutting-edge technologies and methodologies, learning how to develop ontological models that improve the functionality and efficiency of AI applications. The curriculum covers a wide range of topics, from semantic modeling and knowledge graphs to ethical considerations in AI and computational reasoning, preparing students for the challenges and opportunities in the rapidly evolving field of AI.Key Areas of Study
- Formal Ontology and Knowledge Representation
- Semantic Modeling and AI-Driven Knowledge Graphs
- Intelligent Reasoning and Automated Decision Support Systems
- AI Ethics and Intelligent Data Systems
- Computational Logic and Reasoning
Core Curriculum & Program Structure
Program Courses: 120 credits
Degree Requirements
Total Credits Required: 120 credits
Core Major Courses: 40 credits
Electives & Research Focus: 30 credits
General Education & Interdisciplinary Studies: 50 credits
Year One – Foundations of Computational Ontology & AI
Fall Semester 1
COA 101 – Introduction to Computational Ontology & AI (3 credits)
Overview of the principles and applications of computational ontology and AI, including knowledge representation and automated reasoning.
COA 102 – Logic & Formal Systems in AI (3 credits)
Exploration of formal logic and its role in artificial intelligence, with a focus on logical reasoning models.
COA 103 – Fundamentals of Knowledge Representation (3 credits)
Introduction to knowledge representation methodologies, including ontologies, taxonomies, and semantic networks.
General Education Elective (3 credits)
Research & Writing Foundations (3 credits)
Spring Semester 2
COA 104 – Ontological Structures & Semantic Networks (3 credits)
Examination of ontological frameworks and their implementation in AI-driven systems.
COA 105 – AI & Symbolic Knowledge Processing (3 credits)
Study of symbolic AI techniques and how they process and structure knowledge.
COA 106 – Probabilistic Reasoning & Bayesian Inference (3 credits)
Introduction to probabilistic reasoning models and Bayesian inference in AI.
General Education Elective (3 credits)
Introduction to Computational Modeling (3 credits)
Year Two – Intermediate Computational Ontology & AI
Fall Semester 3
COA 201 – Ontology-Based Data Integration & AI Systems (3 credits)
Study of data integration strategies through ontological frameworks and AI.
COA 202 – Machine Learning & Automated Knowledge Extraction (3 credits)
Examination of machine learning techniques used to extract, structure, and manage knowledge.
COA 203 – Cognitive Semantics & Conceptual Structures (3 credits)
Analysis of cognitive semantics and how concepts are structured in AI.
Research Elective in AI & Knowledge Representation (3 credits)
General Education Elective (3 credits)
Spring Semester 4
COA 204 – AI Ethics & Ontological Design Considerations (3 credits)
Ethical considerations in AI development and the role of ontology in decision-making systems.
COA 205 – Epistemic Foundations of AI Knowledge Systems (3 credits)
Exploration of the epistemological principles underlying AI knowledge architectures.
COA 206 – Taxonomies, Ontologies & Schema Design (3 credits)
Development of structured schemas and taxonomies for organizing knowledge in AI.
Research Elective in Computational Ontology & AI (3 credits)
General Education Elective (3 credits)
Year Three – Advanced Computational Ontology & AI
Fall Semester 5
COA 301 – Knowledge Graph Engineering & Linked Data (3 credits)
Practical approaches to designing and implementing knowledge graphs and linked data systems.
COA 302 – AI & Cognitive Automation (3 credits)
Examination of AI-driven cognitive automation systems and their impact on decision-making.
COA 303 – Natural Language Processing & Ontological Semantics (3 credits)
Study of NLP techniques and their integration with ontological models.
Elective in AI, Knowledge Systems, or Cognitive Science (3 credits)
General Education Elective (3 credits)
Spring Semester 6
COA 304 – Intelligent Agents & AI Decision Support Systems (3 credits)
Exploration of intelligent agent frameworks and AI-based decision-making support.
COA 305 – Advanced Ontological Engineering for AI (3 credits)
Study of sophisticated ontological frameworks for AI and complex knowledge systems.
COA 306 – Neural Networks & Machine Learning in Ontology (3 credits)
Investigation of machine learning models applied to ontology-based AI systems.
Research Elective in AI, Knowledge Representation, or Decision Science (3 credits)
General Education Elective (3 credits)
Year Four – Capstone Research & AI Applications
Fall Semester 7
COA 401 – Independent Research in Computational Ontology & AI (3 credits)
Students develop and conduct independent research projects within the field.
COA 402 – AI & the Future of Ontological Systems (3 credits)
A forward-looking exploration of AI trends and their implications for ontology.
COA 403 – Senior Seminar: AI, Logic, & Knowledge Structures (3 credits)
Discussion-based seminar analyzing AI knowledge frameworks and logic-based reasoning.
Research Elective in AI, Knowledge Graphs, or Cognitive Systems (3 credits)
General Education Elective (3 credits)
Spring Semester 8
COA 404 – Capstone Project: AI-Driven Ontological System Development (6 credits)
A final project where students design and implement an AI-driven ontological system.
COA 405 – Computational Intelligence & Semantic Computing (3 credits)
Study of computational intelligence models in AI-driven knowledge management.