1. CS690L: Semantic Web and Knowledge Discovery: Theory, Tools, and
Technology
Winter 2003
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Instructor: Yugi Lee
Phone: 816-235-5932
Office Hours: T/R 3:15 – 4:15pm; by appointment
Class Hours: R 5:30 – 8:15pm
Classroom: FH310
Homepage: http://www.sice.umkc.edu/~leeyu/class/CS690L/cs690l.htm
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The World-Wide Web has revolutionized almost every information-related activity that people
have been pursuing throughout civilized history. The Semantic Web is a vision: the idea of having
data on the Web defined and linked in a way that it can be used by machines not just for display
purposes, but for automation, integration and reuse of data across various applications. If the
vision of a Semantic Web becomes a reality, this would constitute a second revolution that would
impact how we are living our everyday lives. If we succeed in making a step towards the
Semantic Web then our work would have an impact on business, government, education, research
and many other domains that require customized and dynamic Web services and ontology
integration.
The goals of this course are (1) to introduce the concept of Semantic Web, including its
relationship to Ontology and knowledge retrieval, the importance of Semantic Web
representation, including XML, RDF, DAML+OIL, and OWL and their tools; (2) to present an
introduction to theoretical and practical aspects of Knowledge Discovery: understanding various
machine learning and data mining algorithms and techniques for evaluating the performance of
the algorithms (classification, association, clustering, statistical pattern recognition, neural
networks, Bayesian learning, genetic algorithms); (3) Throughout this course, extensive hands-on
exercises in problems in Semantic Web and Knowledge Discovery with various knowledge
retrieval tools, will provide students with a better understanding of the paradigm for Knowledge
Discovery in Semantic Web.
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 Semantic Web and
Knowledge Discovery. Students will be expected to select a problem area in Semantic Web and
Knowledge Discovery and prepare an intensive presentation covering the methods and
framework commonly employed to address their problem.
• Discussion/Presentation: 3 Workshops (one or two presentations/ workshop) 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. Participation will include
such activities as group discussions of topics; discussions with faculty and student groups on
topics, research, and/or application problems; short presentations on relevant papers and
project results; and critiques of resource material, software, and other things related to
semantic web and knowledge discovery.

2. • Critical Reading/Thinking: 30 ~ 50 research paper reading/critique: Students are required to
read and assimilate information from the readings beyond the material covered in class.
Throughout the semester, papers and chapters of the texts will be read and discussed.
• Analytical Writing: 2 - 3 technical reports and one research paper: Students are asked to
think critically and reason about information presented in the books or papers. For example, a
homework assignment might ask how two frameworks we are studying compare, or how
existing technology, like the Web will evolve in the context of knowledge discovery. This
critical evaluation requires that students offer their own understanding of the significance of
what students have learned.
Suggested Pre-requisite: CS551 Advanced Software Engineering
Content of Lectures
1. Workshop 1: Semantic Web: Ontology and Ontology maintenance, Interoperability,
Integration, Composition and Web Services
a. XML and XML Schema
b. RDF and RDF Schema
c. DAML+OIL, DAML-S
d. OWL
e. Others (SHOE, RuleML, etc)
2. Workshop 2: Data Mining and Machine Learning: Prediction, Decision Supporting
and Knowledge Discovery
a. Classification
b. Association
c. Clustering
d. Web Mining
e. Statistical pattern recognition, neural networks, Bayesian learning, genetic
algorithms
3. Workshop 3: Knowledge Discovery in Semantic Web
a. Representation
b. Algorithms
c. Tools
d. Killer Application
Assessment:
• Group Project 40%
• Projects
• Group Activities
• Individual Work 60%
– Papers 40%
– Presentation & Discussion 20%
Both components must be passed in order to pass the course.

3. References:
1. XML Databases and the Semantic Web by Bhavani Thuraisingham, Bhavani Thuraisingha
CRC Press; ISBN: 0849310318 ; 1st edition (March 27, 2002)
2. Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
by Dieter Fensel Springer Verlag; ISBN: 3540416021 ; 1st edition (August 15, 2001)
3. Knowledge Representation: Logical, Philosophical, and Computational Foundations
by John F. Sowa, David Dietz Brooks/Cole Pub Co; ISBN: 0534949657 ; 1 edition (August
17, 1999)
4. Conceptual Spaces: The Geometry of Thought
by Peter Gardenfors MIT Press; ISBN: 0262071991 ; (March 20, 2000)
5. Internet Based Workflow Management: Towards a Semantic Web
by Dan C. Marinescu John Wiley & Sons; ISBN: 0471439622 ; 1st edition (April 5, 2002)
6. 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)
7. Data Mining: Concepts and Techniques by Jiawei Han and Micheline Kamber, ISBN:
1-55860-489-8
8. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementation
by Ian H. Witten and Eibe Frank; ISBN: 1-55860-552-5 Morgan Kaufamann Publisher.
9. Further material will be made available through handouts in class and through pointers to
relevant Web pages.