Knowledge representation

General data

Course ID:	1000-2M11RW
Erasmus code / ISCED:	11.3 The subject classification code consists of three to five digits, where the first three represent the classification of the discipline according to the Discipline code list applicable to the Socrates/Erasmus program, the fourth (usually 0) - possible further specification of discipline information, the fifth - the degree of subject determined based on the year of study for which the subject is intended. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Knowledge representation
Name in Polish:	Reprezentacja wiedzy
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	(in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka Elective courses for Computer Science and Machine Learning Specific programme courses of 2nd stage Bioinformatics
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	elective monographs
Short description:	The course is aimed at introducing students to the conceptual knowledge representation. During classes students will be informed how to use knowledge base methodology in natural language processing, integration of different data models describing the same domains and in construction of semantic search engines. The issues of knowledge visualization and inference based on knowledge bases will be discussed.
Full description:	Knowledge representation in logic. The philosophical foundations of knowledge representation: ontological categories, concrete vs. abstract objects. Concepts and their properties (vagueness, object reference, meaning), propositions, inferences. Principles of knowledge representation and methods of representation: frames, rules, concepts. The differences between the concept based representation of knowledge and representation of knowledge based on logic or rules. Knowledge representation and natural language semantics Conceptual graphs Formal concept analysis: formal contexts, concept lattices and systems of implications Knowledge representation languages, Ontologies and semantic nets Methods of ontology representation Knowledge Bases: CYC, Dbpedia and others Integration of data models Semantic Search
Bibliography:	- John F. Sowa (2000) Knowledge representation. Logical, Philosophical and Computational Foundations, Brooks/Cole. - Dragan Gasević, Dragan Djurić, Vladan Devedzić (2009) Model Driven Engineering and Ontology Development, 2nd edition, Springer. - K.K. Breitman, M.A. Casanova, W. Truszkowski (2007) Semantic Web: Concepts, Technologies and Applications, Springer
Learning outcomes:	Knowledge: 1. has in-depth knowledge of fields of mathematics necessary to represent knowledge (the language of first order logic, lattice theory) (K_W01). 2. understands well the role and importance of construction of formal inferences in knowledge engineering (K_W02). 3. has knowledge about the tools and environments of knowledge representation (K_W10). 4. knows the methods of ontologies construction (K_W02). 5. has a basic knowledge of fundamental ontological categories independent of the field (K_W02). Skills: 1. has the ability to construct a domain model based on informal specification (K_U01). 2. analyzes the formalized concepts in selected logic systems of IT importance, creates in them formalizations of given concepts (K_U10). 3. has in-depth communication skills with experts who do not have IT knowledge (K_U11). 4. can describe selected IT problems and their solutions in a way understandable to non-computer scientist (K_U12). Competence: 1. knows the limits of own knowledge and understands the need for further education, including the acquisition of knowledge from outside the field of computer science (K_K01). 2. is able to precisely formulate questions aimed at deepening his/her own understanding of a given topic, especially in contacts with non-IT specialists (K_K02). 3. can present IT issues to non-IT specialists (K_K06).
Assessment methods and assessment criteria:	- class attendance (at seminars and labs) - doing programming and theoretical homework tasks - written exam - oral exam on the knowledge of theory presented at lectures

This course is not currently offered.

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