CS560: Knowledge Discovery and Management: Theory and Technology

Winter 2006

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Instructor:  Yugyung (Yugi) Lee

Phone: 816-235-5932

Office: FH560D

Office Hours: M/W 3:30 – 4:30pm; by appointment

Class Hours: M 10:00 – 12:25pm

Classroom: FH557

Class webpage: http://www.umkc.edu/blackboard

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The goal of this course is to introduce theoretical and practical aspects of knowledge discovery and management (covering data mining and information extraction). Throughout this course, students will obtain extensive hands-on experiences in problems/solving in knowledge discovery and management and with various tools. In addition, students will be able to apply the concepts and techniques to emerging applications such as Semantic Web, Medical Informatics, Bioinformatics, Mobile and distributed computing, etc.

 

This course will require several distinct types of learning: Since this course is a research-oriented graduate course, a substantial portion of the quarter will be devoted to student presentations of techniques and research papers in the areas of Knowledge Engineering. Students will be expected to select a problem area in Knowledge Engineering and prepare an intensive presentation covering the methods and framework commonly employed to address their problem.

  • Research Project: Students will be asked to design and build an innovative research project for presentation at the end of the semester. Students should organize themselves into research project teams and develop their research project. A final written report will be submitted.
  • Reading/Discussion/Presentation: The lecture/discussions are designed to be highly participatory. Therefore, it is fair and just that points are awarded for effort and participation in these discussions. For each research paper in the assigned reading list: participate in the class discussion of each paper provide written summaries of each paper before class volunteer to present in class certain of the papers on the reading list, on a rotating basis.

 

Prerequisites: Advanced Software Engineering (CS551). There is also a requirement for prior coursework in AI that may be satisfied by either Introduction to Artificial Intelligence (CS461) or Applied Artificial Intelligence (CS464).

 

Assessment:

Research Project                                                                    40%%

Individual Work                                                                       60%

    Paper reviews, Presentation, Participation in discussion     15%

    Labs (5 -6)                                                                         15%

    Exams                                                                                   15%

    In-Class Exercises                                                                15%

            

Content of Lectures (Tentative)

1.       Introduction to Knowledge Engineering

2.       Knowledge Discovery

a.       Machine Learning & Data mining

·         Classification,

·         Clustering,

·         Association

·         Artificial Neural Network,

·         Probabilistic approach,

·         Genetic Algorithm,

b.       Tools for Knowledge discovery

3.       Knowledge Representation

a.       Concept, relations, property

b.       Representation languages

c.       Ontologies

d.       Tools for Knowledge creation  

4.       Knowledge Management

a.       Knowledge mapping and query

b.       Knowledge integration

c.       Knowledge interoperability

d.       Tools for Knowledge Management

5.       Development of Knowledge-based Applications

a.       Semantic Web (Ontology and Web services)

b.       Biomedical Informatics

c.       Medical Informatics

 

Research Projects

Students will be asked to build/create an innovative research project for presentation at the end of the semester. Students will form teams of 1-3 members and work on projects as a team within a particular track. Teams and projects will be decided according to the timeline below. Read ahead to topics that you'd be interested to do a project in. Students are welcome to formulate their own project ideas. Each team will be required to present their project to the class at the end of the course and a final project report written in the style of a conference paper will be handed in following the presentation.

 

Timeline for Project Development:

January 9– February 1: Project teams formed, Literature review, Problem statement

February 6: Project proposal

February - April: Bi-weekly progress reports and meetings with professor/Track leader

(2/22, 3/8, 3/22, 4/19)

April 19 - 26: Final project presentations.

  April 28: Final project reports due.

 

Paper Reviews and Presentations

Students are required to read, present, and discuss graduate-level research papers throughout the semester. An average of 1 paper per week will be read. Written reviews of each paper to be discussed in class are due prior to the start of that class, and should be post to students’ website. Late reviews will not be accepted.

 

Each paper to be discussed in class will be assigned to a student to present in class. Assignments will rotate throughout the class. Papers will be assigned approximately one week in advance of the presentation date. The presenter of a given paper must submit their Powerpoint slides to the blackboard system by midnight of the night before the presentation.

 

Assignments

Students may need to complete reading assignments, exercises, problem sets, review case studies, and engage in implementation and research tasks on Knowledge discovery and management. The late policy on assignments is 10% off the grade if late within one day, 20% off the grade for two days late, 30% off the grade for three days late. Assignments that are submitted more than three days late will no longer be accepted. More information will be available on the Announcements web page.

 

Exams: There will be a number of quizzes and exams through the semester.

 

Policy on Student Attendance and Make-ups:

Each student should make every attempt to get to class on time. The instructor is willing to circulate a sign-in sheet at every class and missing more than two class sessions may result in a reduced grade. With the exception of documented emergencies, medical reasons or out of town travel related to work, make-ups will not be possible. Whenever possible, advance notification is required.

 

University Policy on Student Conduct:

Cheating, plagiarism, disruptive behavior and other forms of unacceptable conduct are subject to strong sanctions in accordance with university policy. See detailed description of university policy at the following URL:

http://www.umkc.edu/html/handbook/policies-and-regulations/conduct.html

 

University policy on English proficiency of Instructors:

"Students who encounter difficulty in their courses because of the English proficiency of their instructors should speak directly with their instructors. If additional assistance is needed, they may contact the UMKC Help Line at 816-235-2222 for assistance."

 

Course material: Journal papers and sections from the following textbooks:

o        Data Mining: Concepts and Techniques by Jiawei Han and Micheline Kamber, ISBN: 1-55860-489-8

o        Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
by Dieter Fensel Springer Verlag; ISBN: 3540416021 ; 1st edition (August 15, 2001)

o         Knowledge Representation: Logical, Philosophical, and Computational Foundations
by John F. Sowa, David Dietz Brooks/Cole Pub Co; ISBN: 0534949657 ; 1 edition (August 17, 1999)

o        Internet Based Workflow Management: Towards a Semantic Web
by Dan C. Marinescu John Wiley & Sons; ISBN: 0471439622 ; 1st edition (April 5, 2002)

o        Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential
by Dieter Fensel (Editor), Wolfgang Wahlster (Editor), Henry Lieberman (Editor) MIT Press; ISBN: 0262062321 ; (November 15, 2002)