Knowledge representation 1000-2M11RW
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
Type of course
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 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
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
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Bioinformatics and Systems Biology
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: