Course catalogue

Systems that represent, organize and utilize knowledge. Semantic Networks, Systems that use frames, systems that use rules, reasoning using rules (forwards and backward chaining), Rete algorithm, design and implementation of rule-based systems. Case-based reasoning. Reasoning under uncertainty. Application of knowledge systems: configuration, design, diagnosis and classification. Introduction to Semantic Web technologies: Structuring XML documents, describing resources using RDF, Ontology Web Language. Logic and reasoning: Rule markup in XML, Applications (Data integration, Information retrieval, Portals, e-Learning, Web Services, etc.). Protégé, an environment for deploying ontologies, Pellet reasoning engine.

On completion of this module, students are expected to be able:

• To have the knowledge of explaining the role of knowledge engineering within Artificial Intelligence, identifing and explaining the various stages in the development of a knowledge based system.
• To have skills of designing and developing a rule-based knowledge based system, designing and developing a case-based knowledge based system, designing and developing Bayesian reasoning systems.
• To posses the capability of understanding the mathematical foundations of Bayesian networks, comparing and contrasting rule- and case-based knowledge based systems, designing and developing Semantic Web concepts and ontologies, comparing and contrasting Semantic Web markup Technologies, and building Ontologies and Reasoning systems in Protégé.

Not required.

Exam (100%)

Greek (English for Erasmus students)