Bachelor of Science in Ontological Engineering – Course Descriptions

An introductory course covering the basic principles of ontological engineering and its application in knowledge-based systems. Students will learn about the role of ontologies in system design and semantic data integration. Topics include the history of ontological engineering, key concepts, and foundational theories. Practical examples and case studies will be used to illustrate the importance of ontologies in various domains.

This course focuses on techniques for building conceptual models that represent real-world domains. Students will explore tools and methodologies for developing ontological frameworks used in knowledge management. The course covers different modeling languages, such as UML and ER diagrams, and their application in creating ontological models. Students will also learn about best practices for ensuring accuracy and consistency in their models.

A practical course on designing systems that rely on ontological knowledge for reasoning and decision-making. Students will build simple knowledge-based systems and learn about expert systems and rule-based logic. The course includes hands-on projects where students will design and implement their own knowledge-based systems, using tools like CLIPS and Jess. Topics also cover the evaluation and validation of these systems.

An exploration of semantic networks and ontology mapping techniques, emphasizing how ontologies can be linked and merged across domains. Topics include ontology alignment and semantic interoperability. Students will study various algorithms and tools for ontology mapping, such as PROMPT and OntoMerge. The course also covers challenges in ontology mapping and strategies to overcome them.

A detailed study of logical systems relevant to ontological engineering, including description logics and non-classical logics. Applications in AI and database design are emphasized. The course covers formal logic, modal logic, and temporal logic, and their use in representing and reasoning about ontological knowledge. Students will also explore the use of logic in automated reasoning and inference.

This course focuses on integrating ontological knowledge into data science workflows and AI applications. Topics include data integration, semantic data modeling, and AI-driven inference. Students will learn how to use ontologies to enhance data preprocessing, feature extraction, and model interpretation. The course includes practical exercises using tools like RDF and SPARQL for semantic data querying.

A practical course on integrating ontologies into larger software systems. Students will learn about middleware, APIs, and best practices for ensuring interoperability. The course covers different integration patterns and techniques, such as service-oriented architecture (SOA) and microservices. Students will work on projects involving the integration of ontological frameworks with existing systems and platforms.

This course examines the role of ontologies in cyber-physical systems, focusing on smart systems, IoT (Internet of Things), and robotics. Real-world case studies are discussed. Students will learn about the challenges and opportunities of using ontologies in these systems, including issues related to real-time data processing, scalability, and security. The course also covers standards and protocols for ontology-based communication in cyber-physical systems.

A specialized course on combining machine learning models with ontological frameworks to enhance reasoning and classification. Applications in natural language processing and data mining are highlighted. Students will explore techniques for integrating ontological knowledge into machine learning pipelines, such as feature engineering and model interpretation. The course includes hands-on projects using tools like TensorFlow and scikit-learn.

An advanced course exploring the intersection of natural language processing (NLP) and ontology. Students will develop ontologies for text mining, sentiment analysis, and semantic search. The course covers techniques for extracting ontological knowledge from text, such as named entity recognition and relation extraction. Students will also learn about the use of ontologies in enhancing NLP applications, such as question answering and information retrieval.

A comprehensive project where students design an ontological solution to a real-world problem. The capstone involves developing a prototype and presenting research findings. Students will work in teams to identify a problem, conduct a literature review, design an ontology, and implement a solution. The course includes regular progress reviews and culminates in a final presentation and demonstration of the project.