Knowledge Engineering

 

Instructor:

Gavrilova T.A., Professor, PhD, DSc, Head of IT in Management Dpt.
E-mail: gavrilova@gsom.pu.ru

Workload:

6 ECTS

45 contact hours

Prerequisites:

No

Course Description:

This course introduces students to the practical application of intelligent technologies into the different business  domains. It will give students insight and experience in key issues of data and knowledge processing in companies. In class and discussion sections, students will be able to discuss issues and tradeoffs in visual knowledge modeling, and invent and evaluate different alternative methods and solutions to better knowledge representation and understanding, sharing and transfer. Lecture course’ goals are focused at using the results of multidisciplinary research in knowledge engineering, data structuring and cognitive sciences into information processing and modern management. The hand-on practice will be targeted at e-doodling with Mind Manager, Visio  and  Cmap software tools.

Course Content (Topics and subtopics):

The course features the knowledge engineering as the practical methodology of company knowledge processing and will be defined as a set of techniques to manage big amounts of personal and corporate information. The stress will be put at visual methods as mind mapping and concept-mapping. The course examines a number of related topics, such as:

Topic 1. Brief Introduction to Systems Analysis and Information management. Systems, elements, relations, hierarchy.

Topic 2. Visual Approach to Knowledge Engineering (KE). Knowledge and data. Practical knowledge structuring: visual approach. Mental models. Mind maps and mind-mapping tools. Concept maps and tools. Roadmaps and knowledge maps. E-doodling.

Topic 3. Knowledge representation models in business practice. Knowledge models classification. Knowledge engineer and development team. Portrait of knowledge engineer and knowledge manager: psychological and professional profile.

Topic 4. Theoretical issues and Practical aspects of KE. Psychological, linguistic and methodological issues. Classification and practice of KE methods. Knowledge structuring techniques. Knowledge Representation.

Topic 5. Ontological Engineering. Semantic ontology design: step by step. Algorithms and tips for visual design of ontologies. Ontologies as a kernel of knowledge management. Taxonomy and development of corporate ontologies. Visual tools for ontology development.

Topic 6. Knowledge Management (KM). Traditional approach and definition. Social and organizational aspects of KM. Cognitive problems of KM. Knowledge sharing techniques.

Topic 7. Corporate memory. Corporate knowledge lifecycle. IT-Tools for KM. KM management and company culture: modern examples and case study.

Topic 8. Introduction to Information Management. Short history. Knowledge-based systems. Expert systems. Machine learning. Data mining and Knowledge discovery. Artificial Intelligence.

Course Organization:

The class will feature lectures, cases, exercises, discussions, short tests and, students will have several hand-on practices using mind-mapping and concept mapping software. Lectures will be important but the emphasis will be on learning through training, games, discussions and short tests. Students will make short (5-7 minutes) presentations and write a brief analytical essay (7-10 pages). A good deal of the course will focus on auto-reflection and auto-formalizing of knowledge, training of analytical and communicative abilities, discovery, creativity, achieving new perspectives, synthesizing evidence from disparate sources, and gaining new insights in this fascinating new field. All practical exercises will be done in computer class using Visio, Mindjet and  map software tools. Class should not exceed more than 24-28 students.

Course Reading (the full list):

Core reading:
• Gavrilova T., Zhukova S., Leshcheva I. Knowledge Engineering. Learning guide, GSOM, 2016.
• Greetham, Bryan. Thinking Skills for Professionals. 2010. http://vufind.gsom.spbu.ru/vufind/Record/978140391708-9

Supplementary reading:
• Nast J. Idea Mapping: How to Access Your Hidden Brain Power, Learn Faster, Remember More, and Achieve Success in Business. Wiley, 2006.
• Okada A., Shum B. S., Sherborne T. (Eds) Knowledge Cartography: Software Tools and Mapping Techniques (Advanced Information and Knowledge Processing). Springer, 2008.
• Schuster P.M. Concept Mapping: A Critical-Thinking Approach to Care Planning, F. A. Davis Company, 2007.

Exam format:

In-class
Duration: 90 minutes

Grading Policy (% or points):

Final exam – 50%, 20 % - fulfilment of obligatory assignments (E-portfolio), 10% - class activity, 5% -paper, 5% -presentation, 10%-midterm examination test.
Timeline: Midterm November 21, 2017; Portfolio part 1 November 23, 2017, Essay December 2, 2017, Portfolio part 2 December 12, 2017, Exam December 19, 2017.

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