Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Licenţă
Domeniul: Informatică
Programul de studii: Informatică - în limba engleză
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MID0043	Knowledge Processing	

Semester	Hours: C+S+L+P	Category	Status	
6	2+0+1+1	speciality	optional	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	Title		C	S	L	
SACAREA Cristian	Ph.D.	Lect.	Programming  Languages and Methods	*			

Associated faculty members
Name and	Scientific	Institution	Type of	Type of activity	
surname	title		position	C	S	L	

III. Course objectives
This course aims to give students a basic understanding about the fundamental principles, concepts and challenges underlying knowledge processing and management . At the end of this course, students will have a thorough theoretical understanding of these issues, and the ability to relate and apply knowledge processing and management theories in the light of simple examples. Selected case studies are used to illustrate the utility of knowledge processing and management theories in specific situations, but also to highlight current gaps between knowledge processing and management theory and practice.	

IV. Course contents
1.	Overview and Motivation
2.	Knowledge Types. Can Knowledge Be Processed?
3.	From Knowledge Processing to Knowledge Management.
4.	Knowledge Organization.
5.	Knowledge Bases.
6.	Formal Concept Analysis.
7.	Knowledge Acquisition.
8.	Knowledge Representation.
9.	Knowledge Transfer.
10.	Knowledge Repositories.
11.	Bussiness Oriented Knowledge Management.
12.	Knowledge-Based Analysis.

V. Bibliography
1.   Bernhard Ganter, Rudolf Wille, Formal Concept Analysis, Springer Verlag, 2000
2.	Aldo de Moord, Wilfried Lex, Bernhard Ganter, eds., Conceptual Structures for Knowledge Creation and Communication, Springer LNAI 2746, 2003.
3.   R. Maier, Knowledge Management Systems, Springer 2007.
4.   H. Rollett, Knowledge Management, Processes and Technologies, Kluwer Academic Publishing, 2003.  

VI. Thematic of didactic activities per weeks
1.	Overview and Motivation (Week 1): Basic motivation and introduction to this course.
Readings: P.F. Drucker, Knowledge-Worker Productivity: The Biggest Challenge, California Management Review 41 79--94 (1999).
2.  Knowledge Types. Can Knowledge Be Processed? (Week 2 ): What is knowledge? What forms of knowledge can we identify? We will discuss some basic distinctions and characterizations. 
Readings: D. Kirsh, When is information explicitly represented?, Information, Language and Cognition - The Vancouver Studies in Cognitive Science.: 340--365, 1990.
Lab 1: Data – Information – Knowledge.
3.  From Knowledge Processing to Knowledge Management. (Week 3):  What is Knowledge Processing. How can knowledge be processed? We will discuss a series of knowledge management perspectives and their implications. 
Readings: M.T. Hansen and N. Nohria and T. Tierney, What's your Strategy for Managing Knowledge?, Harvard Business Review, 1999. 
M. Earl, Knowledge Management Strategies: Toward a Taxonomy, Journal of Management Information Systems 18 215--233, 2001.
4.  Knowledge Organization (Week 4): How can knowledge be organized? We will discuss some basic principles of knowledge organization, such as categorization, taxonomies and concept systems.
Readings: C.B. Mervis and E. Rosch, Categorization of Natural Objects, Annual Review of Psychology 32 89--115, 1981.
Lab 2: Knowledge clustering.
5.  Knowledge Bases (Week 5): What kinds of broad knowledge bases exist? We will discuss different forms of knowledge bases and representations, such as metadata, wordnet, framenet, cyc, openmind and others. 
Readings: T. Berners-Lee and J. Hendler and O. Lassila, The semantic Web, Scientific American, 284 (5) 2001.
6. Formal Concept Analysis (Week 6): An introduction to the basic model of FCA. We discuss the main representation of a data set as a formal concept and the knowledge clustering called concept hierarchy and its importance to knowledge processing. 
Lab 3: From context to concept. 
7. Formal Concept Analysis (Week 7): Many valued contexts,  conceptual scaling, logical scaling. Examples.
Readings: Formal Concept Analysis: Methods and Applications in Computer Science, Bernhard Ganter, Adapted and extended by Gerd Stumme, Summer 2003.
8. Formal Concept Analysis (Week 8): The conceptual hierarchy as a knowledge map.
Lab 4: Constructing knowledge maps using Toscana.
9. Knowledge Acquisition (Week 9): How can knowledge be acquired in a way that is amenable to computation and/or analysis? We also discuss implications in data sets and algorithm to determine the stem base.
Readings: L. von Ahn, Games with a Purpose, Computer, 39(6): 92--94, 2006.
10. Knowledge Transfer (Week 10): How can knowledge transfer be characterized and what factors can influence knowledge transfer? 
Readings: M. E. Nissen and R. E. Levitt, Agent-Based Modeling of Knowledge Flows: Illustration from the Domain of Information Systems Design, Proceedings of the 37th Hawaii International Conference on System Sciences, 2004.
A. Cabrera and E.F. Cabrera, Knowledge-sharing dilemmas, Organization Studies 23 687-710 (2002) 
Lab 5: Conceptual and logical scaling.
11. Knowledge Repositories (Week 11): How can knowledge repositories be designed and deployed? We will discuss concepts such as knowledge reuse, discretionary databases, experience factories and selected concepts from case based reasoning.
Readings: V. R. Basili and G. Caldiera and D.H. Rombach, Experience Factory, Encyclopedia of Software Engineering 469-476 (1994)
12. Bussiness Oriented Knowledge Management (Week 12): In this class, we will discuss different approaches aimed at integrating knowledge management into an organization's business processes. 
Readings: U. Remus and F. Lehner. The Role of Process-oriented Enterprise Modeling in Designing Process-oriented Knowledge Management Systems. Proceedings of the AAAI Symposium on Bringing Knowledge to Business Processes. Stanford, CA, USA, 2000.
Lab 6: Knowledge Processing in Bussiness.
13. Knowledge Based Analysis (Week 13-14):  How can socio-technological systems be organized from a knowledge perspective? We will discuss an agent-oriented modeling approach for analyzing knowledge transfer instruments. 
Readings: M. Strohmaier, E. Yu, J. Horkoff, J. Aranda and S. Easterbrook, Analyzing Knowledge Transfer Effectiveness – An Agent-Oriented Approach, In Proceedings of the 40th Hawaii International Conference on System Sciences (HICSS-40 2007), January 3-9, IEEE Computer Society, Hawaii, USA, 2007.
Lab 7: Knowledge Management tools.

VII. Didactic methods used
Lectures, presentations, conversations, projects, exercises, individual study, homework assignments.

VIII. Assessment
Practical project.	

IX. Additional references
See readings list in chapter VI.	

		Dean,							Department head,
Prof. Dr. Leon Tambulea                                                     Prof. Dr. Parv Bazil

	Date,							Course responsible,
October 1, 2011						Lect. SACAREA Christian, Ph.D.