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

SYLLABUS

I. General data
Code	Subject	
MML1016	Knowledge Discovery in Wide Area Networks	

Semester	Hours: C+S+L	Category	Status	
1	2+2+0	speciality	compulsory	

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

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

III. Course objectives
•	To introduce the student to a broad range of information representation models drawn from the fields of information science, computer science, semiotics, philosophy, cognitive psychology, and artificial intelligence.
•	To introduce a formal method of qualitative data analysis. 
•	To provide practical experience with basic data analysis techniques, such as selection, grouping and scaling of features. 
•	To develop the student's ability to understand the problems involved in the formalization of informal data. 
•	To teach practical skills of using the computer software DIAGRAM, ANACONDA, and TOSCANA. 
•	To provide practical experience with techniques of structuring graphical representations.
To provide insights into the formal structure of classification systems.	

IV. Course contents
•	Introduction: Data, Information, and Knowledge
•	Classification, categorization, and concepts
•	Cognitive Organization of Information
•	Introduction in Knowledge Management
•	Formal Modeling of Data
•	Conceptual Knowledge Representation
•	Formal Concept Analysis
•	Concept Hierarchies from Contexts
•	Conceptual Knowledge Inferences
•	ANACONDA and DIAGRAM
•	Conceptual Scaling
•	Nested concept hierarchies and TOSCANA
•	Conceptual Knowledge Aquisition
•	Applications

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.	Bernhard Ganter, Aldo de Moord, eds., Using Conceptual Structures, Shaker Verlag, 2003.
4.	Frank Vogt, Formal Concept Analysis with C++, Springer, 1996
Rokia Missaoui, Jürg Schmid, eds., Formal Concept Analysis, Springer LNAI 3874, 2006.	

VI. Thematic of didactic activities per weeks
1.  Introduction: Data, Information, and Knowledge (1 lecture – Week 1 + 1 seminar – Week 1)
	
•	What is data? Information? Knowledge? How are they represented? 
•	Information access and information usage 
•	The interdisciplinarity of information science.
Readings: 
Wurman, Richard Saul (1989). 
The Understanding Business. In: Information Anxiety - What to do when information doesn't tell you what you need to know. New York: Doubleday. p. 51-82. 
Buckland, Michael (1991). 
Information as thing. Journal of the American Society for Information Science, 42, p. 351-360. 

2. Classification, categorization, and concepts  (1 lecture – Week 2 + 1 seminar – Week 2)
	·  What is categorization or classification? 
·  The cultural and cognitive nature of categorization. 
·  Philosophical theories of classification and concepts.
Readings: 
Jacob, Elin K. (1991). 
Classification and categorization: drawing the line. In: Barbara H. Kwasnik and Raya Fidel (Eds.). Advances in classification research. Vol. 2, Washington D.C.: American Society for Information Science, p. 67-83. 

3. Cognitive Organization of Information  (1 lecture – Week 3 + 1 seminar – Week 3)
•	The mental lexicon and information organization/access
•	Procedural knowledge (knowing "how") versus declarative knowledge (knowing "what"); procedural knowledge can be non-verbal.
•	Semantic memory (topic, definitions, "textbook knowledge") versus episodic memory (experience, history, "expert knowledge").
Readings: 
Robillard, Pierre (1999). 
The role of knowledge in software development. Communications of the ACM, January 1999, p. 87-92. 
Optional Reading: Rumelhart, David E. (1984). 
Schemata and the cognitive system. In: Wyer and Srull (Eds.). Handbook of social cognition. Vol. 1, Hillsdale NJ: Lawrence Erlbaum, p. 161-188. 
Optional Reading: Schank, Roger, and Kass, Alex. (1988). 
Knowledge representation in people and machines. In: Umberto Eco, Marco Santambrogio and Patrizia Violi (Eds.), Meaning and mental representation. Bloomington: Indiana University Press, p. 181-200. 

4. Introduction in Knowledge Management (1 lecture – Week 4 + 1 seminar – Week 4)

•	Acquisition, representation and computing of knowledge.
•	Knowledge bases for constructions
Readings: 

Russel: Artificial Intelligence: A Modern Approach, Prentice Hall, 2002
[5], pp. 1 – 29.

5. Formal Modeling of Data  (1 lecture – Week 5 + 1 seminar – Week 5)
•	How can informal data be formally investigated?
•	Data selection, coding and representation
•	Qualitative versus quantitative data analysis
•	Create a list of problems involved in formal data analysis methods. Assign the problems to the different stages of data analysis (such as data selection, coding and representation). Try to evaluate the severity of each problem. 
Readings:
Wolff, Karl Erich (1995) 
Comparison of Graphical Data Analysis Methods. Proceedings SoftStat'95. (Hint: It is not necessary to understand the mathematical details of this paper. Study the "main problems" mentioned for the data analysis methods.) 
Schoenemann, Peter H. (1994) 
Measurement: The Reasonable Ineffectiveness of Mathematics in the Social Sciences. In: Borg; Mohler (eds.). Trends and Perspectives in Empirical Social Research. De Gruyter. pp. 149 - 159. 
de Leeuw, Jan (1994) 
Statistics and the Sciences. In: Borg; Mohler (eds.). Trends and Perspectives in Empirical Social Research. De Gruyter. pp. 138 - 148. 
Optional Readings:
Gigerenzer, Gerd; Murray, David J. (1987) 
Emergence of Statistical Inference. In: Cognition as Intuitive Statistics. Lawrence Erlbaum, Hillsdale, New Jersey. pp. 1 - 28. 
Paulos, John Allen (1988) 
Innumeracy : mathematical illiteracy and its consequences. New York, Hill and Wang. (Hint: This book is not directly concerned with data analysis, but with basic mathematical operations, such as counting and estimating. The mistakes which Paulos describes are more likely to be found in newspaper statistics than in scientific research.) 

6. Conceptual Knowledge Representation  (1 lecture – Week 6 + 1 seminar – Week 6)
•	Information Representation in AI and Cognitive Science
•	Semantic Networks
•	KL-ONE and description logics
•	Concept based vs. linguistic representation
•	Knowledge Representation Systems
•	Challenges for reasoning strategies and knowledge representation
Readings: 
Firebaugh, M. W. (1988) 
Knowledge Representation in AI. Artificial Intelligence. Chapter 9. Boston: Boyd & Frasier. p. 274-299.

7.	Formal Concept Analysis (1 lecture – Week 7 + 1 seminar – Week 7)
•	A formal model for concepts
•	Designing formal contexts
•	Extracting concepts from formal contexts
•	Concept lattices

Readings:
Wille, Rudolf (1996). 
Introduction to Formal Concept Analysis. Preprint, TH-Darmstadt. pp 1-4 
Wolff, Karl Erich (1994) 
A first Course in Formal Concept Analysis. Proceedings SoftStat'93. Gustav Fischer Verlag. pp 1-5 

8. Concept Hierarchies from Contexts  (1 lecture – Week 8 + 1 seminar – Week 8)
•	Representation of a Concept Hierarchy by a Line Diagram
•	How to draw nice line diagrams
•	Checking a Line Diagram of a Concept Hierarchy
•	Dualizing a Concept Hierarchy
•	Concept Patterns in a Concept Hierarchy
Readings: 
[1] Chapter 0, 1
[5] pp. 1 – 29.

9. Conceptual Knowledge Inferences  (1 lecture – Week 9 + 1 seminar – Week 9)
•	Implications between Attributes
•	Dependencies between Attributes
•	Stem base
Readings: 
[1] Chapter 2

10. ANACONDA and DIAGRAM  (1 lecture – Week 10 + 1 seminar – Week 10)
•	The software ANACONDA and DIAGRAM 
•	Drawing lattices with a computer
•	Identifying Concept Patterns
Readings:
Luksch; Skorsky; Wille (1985) 
On Drawing Concept Lattices with a Computer. In: Gaul; Schader (eds.). Classification as a Tool of Research. North-Holland. pp. 269 - 274. 

11. Conceptual Scaling  (1 lecture – Week 11 + 1 seminar – Week 11)
•	Conceptual Scaling of a Context
•	Conceptual Scaling of a Many-valued Context
•	Different types of scales
•	Conceptual Classification
Readings:
[5], pp. 1 – 29
[1] Chapter 1, 7
Wolff, Karl Erich (1994) 
A first Course in Formal Concept Analysis. Proceedings SoftStat'93. Gustav Fischer Verlag. pp 5-9 
Vellemann, Paul; Wilkinson, Leland (1994) 
Nominal, Ordinal, Interval, and Ration Typologies are Misleading. In: Borg; Mohler (eds.). Trends and Perspectives in Empirical Social Research. De Gruyter. pp. 161 - 177. 
Optional Reading:
Ganter, Bernhard; Wille, Rudolf (1989) 
Conceptual Scaling. In: Roberts (ed.). Applications of combinatorics and graph theory to the biological and social sciences. Springer, Heidelberg. 

12. Nested concept hierarchies and TOSCANA  (1 lecture – Week 12 + 1 seminar – Week 12)
•	Nested Line diagrams
•	Citation order
•	The software TOSCANA
Readings:
Wille, Rudolf (1996). 
Introduction to Formal Concept Analysis. Preprint, TH-Darmstadt. pp 6-7 
Scheich; Skorsky; Vogt; Wachter; Wille (1993). 
Conceptual Data Systems. In: Opitz; Lausen; Klar (eds.). Information and Classification. Springer, Berlin-Heidelberg-New York. 
Vogt, Frank; Wille, Rudolf (1995) 
TOSCANA - A Graphical Tool for Analyzing and Exploring Data. In: Tamassia; Tollis (eds.). Graph Drawing. Springer, Heidelberg. 
Skorsky, Martin 
TOSCANA Management System for Conceptual Data. Available at:
http://www.mathematik.th-darmstadt.de/ags/ag1/software/ToscanaDemo/ToscanaDemo.html 

13. Conceptual Knowledge Aquisition (1 lecture – Week 13 + 1 seminar – Week 13)
•	Attribute Exploration
•	Concept Exploration
•	Discovering Association Rules

Readings:
[1], Chapter 2
R. Wille, 
Knowledge Acquisition by methods of Formal Concept Analysis, E. Diday (ed.) Data Analysis and learning symbolic and numeric knowledge, Nova Science Publisher, 1989, 33 – 58.
G. Stumme, 
Concept Exploration: knowledge exploration in conceptual knowledge systems. Dissertation, TU Darmstadt, 1997.

14. Applications (1 lecture – Week 14 + 1 seminar – Week 14)
•	Conceptual Knowledge Retrieval
•	Organizing, searching, mining information and knowledge
•	Improving individual search strategies
•	Hosting multiple integrated search strategies
Readings:
1.	B. Ganter, G. Stumme, R. Wille, eds.
	Formal Concept Analysis: foundations and applications, Springer LNAI 3626, 2005
2.   P. Becker, J. Hereth Correia:
	The ToscanaJ Suite for implementing conceptual information systems, in 1, pp. 324 - 348
3.   C. Carpineto, G. Romano, 
	Concept data analysis: theory and applications, Wiley, 2004.
4.   C. Carpineto, G. Romano, 
	Using concept lattices for text retrieval and mining, in 1, pp. 161- 179

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

VIII. Assessment
The activity ends with a written final exam (grade E) and a practical project during the semester (grade P). The exam subjects have theoretical questions from all the studied topics, and one problem, among the problems studied at the course and seminar. The final grade arithmetical mean of the two grades mentioned above, conditioned by all the grades being at least 5. Otherwise, the exam will not be passed. The final grade = (E + P)/2.

IX. Additional references
See readings list in chapter VI.	

Dean,                                                                                      Chair head,
Prof. Dr. Leon Tambulea                                                     Conf. Dr. Simion Breaz

Date,							Course responsible,
March 3, 2009						Lect. SACAREA Cristian, Ph.D.