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	
MML1017	Knowledge Discovery and WEB Semantics	

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

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
SACAREA Christian	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
•	Making available a broad range of methods for Knowledge Representation and Processing, and Knowledge Engineering.
•	To discover the underlying formal structure of knowledge, to derive knowledge. 
•	Knowledge landscape paradigm. 
•	To develop the student's ability to understand the problems involved in the formalization of informal data. 
•	To teach practical skills for representing knowledge by computers. 
•	To provide practical experience with techniques of structuring graphical representations.
•	To provide techniques for preprocessing data sets, including the concept of a Data Warehouse
•	To provide models for automatically learning methods.

IV. Course contents
•	Introduction: Conceptual Knowledge Processing
•	Conceptual Theory Building
•	Formal Concept Analysis
•	Conceptual Graphs
•	Power Context Families
•	Ontologies
•	Knowledge Discovery and Data Mining
•	Knowledge Discovery in Databases
•	Decision Trees
•	Neuronal networks
•	Association rules
•	Knowledge Representation
•	Semantic Web
•	Applications

V. Bibliography
1.	M. Ester und J. Sander: Knowledge Discovery in Databases: Springer-Verlag, 2000. 
2.	U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurasamy: Advances in Knowledge Discovery and Data Mining. Cambridge , London . MIT Press, 1996.
3.	Frank Vogt, Formal Concept Analysis with C++, Springer, 1996
4.	Rokia Missaoui, Jürg Schmid, eds., Formal Concept Analysis, Springer LNAI 3874, 2006.
5.	B. Ganter, R. Wille, Formal Concept Analysis, Mathematical Foundations, Springer 2000.
6.	G. Antoniou, F. van Harmelen: A Semantic Web Primer. MIT Press, Cambridge 2004.
7.	Online Course in Knowledge Representation using Conceptual Graphs, Aalborg University, Department of Communication.
8.	J. Sowa: Knowledge Representation, Logical, Philosophical, and Computational Foundations. Brooks/Cole, Pacific Grove 2000.

VI. Thematic of didactic activities per weeks
1.  Introduction: Conceptual Knowledge Processing (1 lecture – Week 1 + 1 seminar – Week 1)
	
•	The Conceptual Landscape Paradigm
•	Conceptual Classification
•	Analysis of Concept Hierarchies.
Readings: 
[4] 1 – 29

R. Wille:
Conceptual landscape of knowledge: a pragmatic paradigm for knowledge processing. In: G. Mineau, A. Fall, eds. : Proceedings of the International Symposium on Knowledge Representation, Use, and Storage Efficiency. Simon Fraser University, vancouver 1997, 2-13

2. Conceptual Theory Building  (1 lecture – Week 2)
•	Theory Building with Concept Hierarchies
•	Theory Building with TOSCANA
•	Contextual Logic
Readings: 
[4] 1 – 29

R. Wille:
	Truncated distributive lattices: conceptual structures of simple implicational theories. Order 20 (2004), 229 – 238.

[5] Chapter 2,3.

K. Mackensen, U. Wille: 
	Qualitative text analysis supported by conceptual data systems, Quality & Quantity 33 (1999), 135 -156.

3. Formal Concept Analysis (1 lecture – Week 3 + 1 seminar – Week 3)
•	Decomposition of Concept Hierarchies
•	Generating all Concept Within a Line Diagram.
•	Atlas Decomposition of a Concept Hierarchy.
•	Aggregation of Contexts and Concepts Hierarchies
Readings: 
[5] Chapters 2,3,4

4. Introduction in Knowledge Management (1 lecture – 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. Conceptual Graphs  (1 lecture – Week 5 + 1 seminar – Week 5)
•	John Sowa's conceptual graphs 
•	Structures and applications of conceptual graphs 
Readings: 
Sowa, John (1999). 
Conceptual Graphs and Conceptual Graphs Examples (on-line documents). 
Wolff, Karl Erich (1994). 
A first Course in Formal Concept Analysis. Proceedings SoftStat'93. Gustav Fischer Verlag. p. 1-5. 

6. Power Context Families  (1 lecture – Week 6)
•	Contextual Judgement Logic
•	Semantics of Conceptual Graphs
•	Concept Graph of a Power Context Family
•	Conceptual Contents
Readings: 
Aldo de Moor, W. Lex, B. Ganter, eds:
	Conceptual Structures for Knowledge Creation and Communication, 1 – 15.
R. Wille:
	Conceptual Graphs and Formal Concept Analysis. In: D. Lukose, H. Delugach, M. Keeler, L. Searle, J.F. Sowa, eds.: Conceptual structures: fulfilling Peirce’s dream. LNAI 1257, Springer, 1997, 290 – 303.

7.	Ontologies (1 lecture – Week 7 + 1 seminar – Week 7)
•	Common sense knowledge and language
•	CYC and Ontolingua 
•	Ontologies in E-commerce 
•	Applications

Readings: 
Lenat, Douglas B. (1995). 
CYC: a large-scale investment in knowledge infrastructure. In: Communications of the ACM 38 (11), November 1995, p. 33-38. 
The Upper CYC Ontology. 
(Online document). 
Optional reading: Ontology FAQ 
(Online document). 
Optional reading: Agent-Oriented Technology in Support of E-Business 
In: Communications of the ACM, Vol 44, 4, 2001. 

8. Knowledge Discovery and Data Mining  (1 lecture – Week 8)
•	Knowledge discovery versus information retrieval 
•	Strategies and limits 
•	Applications

Readings: 
Fayyad, U.; Uthurusamy, R. (1996)
Data Mining and Knowledge Discovery in Databases. Communications of the ACM, Vol. 39, No. 11, p. 24-26. 
Munakata, Toshinori (1999) 
Knowledge Discovery. Communications of the ACM, Vol. 42, No. 11, p. 27-29. 
Optional Readings: 
Communications of the ACM, Vol. 39, No. 11, p. 27-64. 
Communications of the ACM, Vol. 42, No. 11, p. 30-67. 
Optional reading: The Semantic Web 
In: Scientific American, May 2001. 

9. Knowledge Discovery in Databases (1 lecture – Week 9 + 1 seminar – Week 9)
•	Data selection
•	Data clarifying
•	Operations on contexts
•	Data reducing
Readings:  [1], Chapter 1

10. Decision Trees  (1 lecture – Week 10)
•	Decision Trees construction
•	Decision Trees rules and prunning
•	Decision Trees representation
Readings:  [1], Chapter 2,6

11. Neuronal networks  (1 lecture – Week 11 + 1 seminar – Week 11)
•	Parallel distributed processing network
•	Types of neuronal networks
•	Genetic algorithms
•	Neuro-fuzzy
Readings:
[2] Chapter 5 

12. Association rules  (1 lecture – Week 12)
•	Formal Concept Analysis tools
•	Algorithms
•	Generalized Association Rules
Readings:
Wille, Rudolf (1996). 
Introduction to Formal Concept Analysis. Preprint, TH-Darmstadt. pp 6-7 
[5] Chapter 5

G. Stumme, 
Concept Exploration: knowledge exploration in conceptual knowledge systems. Dissertation, TU Darmstadt, 1997.

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.

13. Knowledge Representation  (1 lecture – Week 13 + 1 seminar – Week 13)
•	Description logics
•	Concept Exploration
•	Discovering Association Rules

Readings:
[1], Chapter 2

14. Semantic Web  (1 lecture – Week 14)
•	Semantic Web Impact
•	Knowledge Management
•	Semantic Web technologies
•	OWL
Readings: [6]

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

VIII. Assessment
Project.	

IX. Additional references
See readings list in chapter VI.

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

	Date,							Course responsible,
October 1, 2009						Lect. SACAREA Cristian, Ph.D.