509480 - KNOWLEDGE REPRESENTATION AND REASONING - MOD. 2

Learning Objectives (according to the Dublin Descriptors) Knowledge and understanding (DdD 1) By the end of the course, students will have acquired theoretical knowledge of: • the principles and challenges of knowledge representation and reasoning (KRR) in artificial intelligence; • discrete mathematical structures relevant to KRR, such as order relations and lattices; • foundational concepts of relational databases, including relational algebra and SQL; • logic programming (e.g., Datalog, Answer Set Programming), with a focus on non-monotonic and disjunctive reasoning; • the semantics, syntax, and applications of knowledge graphs, including key technologies such as RDF, SPARQL, RDFS, and OWL2; • the distinction and integration between logic-based and graph-based approaches to knowledge modeling; • the theoretical underpinnings of constructing and reasoning over ontologies using Description Logics. Applying knowledge and understanding (DdD 2) Students will be able to: • design, develop, and query knowledge bases using appropriate logic programming tools (e.g., DLV for ASP and Datalog); • model domain knowledge with RDF and OWL, and query data using SPARQL; • implement non-monotonic reasoning tasks using logic programming paradigms; • apply principles of database design to knowledge-intensive systems; • use reasoning systems to infer new knowledge from explicit facts; • develop and debug knowledge-based systems by integrating foundational theory with practical tools and technologies; • solve real-world KRR problems through lab exercises and small applied projects. Other Dublin Descriptors (DdD 3, 4, 5) • Making judgments (DdD 3): Through exercises and QA sessions, students will cultivate critical thinking in selecting and evaluating appropriate representation and reasoning approaches for various problem domains. • Communication skills (DdD 4): Students will improve their ability to clearly communicate knowledge models and reasoning strategies during QA sessions. • Learning skills (DdD 5): The course fosters independent learning by providing conceptual foundations and practical experience that support further study and specialization in advanced KRR topics or related AI fields.