|Μηχανική Γνώσης και Συστήματα Γνώσης|
|Title: ||Μηχανική Γνώσης και Συστήματα Γνώσης|
|Lesson Code: ||321-7406|
|Theory Hours: ||3|
|Lab Hours: |
|Faculty: ||Maragkoudakis Emmanouil|
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 explain the role of knowledge engineering within Artificial Intelligence, to identify and explain the various stages in the development of a knowledge based system, to design and develop a rule-based knowledge based system, to design and develop a case-based knowledge based system, to design and Develop Bayesian reasoning systems, to understand the mathematical foundations of Bayesian networks, to compare and contrast rule- and case-based knowledge based systems, to design and develop Semantic Web concepts and ontologies, to compare and contrast Semantic Web markup Technologies, and to build Ontologies and Reasoning systems in Protégé.
1. Semantic Web Primer, Grigoris Antoniou, Frank Van Harmelen, Kleidarithmos Publications, ISBN:978-960-461-234-5, 2009.
2. Introduction to Artificial Intelligence and Agent Systems, Ν. Matsatsinis - Ν. Spanoudakis - Α. Samaras, Neon Technologion Publications, ISBN:960-8105-77-3, 2006.
1. Semantic Web for the Working Ontologist, Second Edition: Effective Modeling in RDFS and OWL, Dean Allemang, James Hendler, Morgan Kaufmann, ISBN: 978-0123859655, 2011.
2. Modeling and Reasoning with Bayesian Networks , Adnan Darwiche, Cambridge University Press, ISBN: 978-0521884389, 2009.
3. Knowledge Representation and Reasoning, Ronald Brachman, Hector Levesque, Morgan Kaufmann, ISBN: 978-1558609327, 2004.
4. Knowledge and Representation, by Albert Newen (Editor), Andreas Bartels (Editor), Eva-Maria Jung (Editor), Center for the Study of Language and Inf, ISBN: 978-1575866307, 2011.
|Learning Activities and Teaching Methods |
Appart from lectures and electronic educational material through the Department’s e-learning platform, extra seminars are given by the tutor, as laboratory learning material, which contains information on learning XML, RDF and OWL formats, as well as in Protégé and Bayesian Networks.
|Assessment/Grading Methods |
Participants write from 3-5 exercises, either programming or research-oriented. The exercises are not compulsory but have a grade weight of 50% of the final grade. Alternative, if participants choose not to do their exercises, their grade comes from the final exam 100%.
|Language of Instruction|
|Greek, English (for Erasmus students)|
|Μode of delivery |