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CS690L Semantic Web and Knowledge Discovery: Concept, Technologies, Tools

CS690L Semantic Web and Knowledge Discovery: Concept, Technologies, Tools. Yugi Lee STB #555 (816) 235-5932 leeyu@umkc.edu www.sice.umkc.edu/~leeyu. General stuff. Class hours : R 5:30 – 8:15pm FH310 Office hours : Yugi Lee: T/TH 3:15-4:15 or by appointment

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CS690L Semantic Web and Knowledge Discovery: Concept, Technologies, Tools

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  1. CS690LSemantic Web and Knowledge Discovery: Concept, Technologies, Tools Yugi Lee STB #555 (816) 235-5932 leeyu@umkc.edu www.sice.umkc.edu/~leeyu CS690L - Lecture 1

  2. General stuff • Class hours: R 5:30 – 8:15pm FH310 • Office hours: • Yugi Lee: T/TH 3:15-4:15 or by appointment • Class page: www.sice.umkc.edu/~leeyu • Lecture notes will be available in advance • Mailing List • Suggested Prerequisites • CS551 (Advanced Software Engineering) • Middleware Technology, component-based model (UML), Object-Oriented programming language (Java, C++) CS690L - Lecture 1

  3. Reference books • XML Databases and the Semantic Web by Bhavani Thuraisingham, Bhavani Thuraisingha CRC Press; ISBN: 0849310318 ; 1st edition (March 27, 2002) • Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerceby Dieter Fensel Springer Verlag; ISBN: 3540416021 ; 1st edition (August 15, 2001) • Knowledge Representation: Logical, Philosophical, and Computational Foundationsby John F. Sowa, David Dietz Brooks/Cole Pub Co; ISBN: 0534949657 ; 1 edition (August 17, 1999) • Conceptual Spaces: The Geometry of Thoughtby Peter Gardenfors MIT Press; ISBN: 0262071991 ; (March 20, 2000) • Internet Based Workflow Management: Towards a Semantic Webby Dan C. Marinescu John Wiley & Sons; ISBN: 0471439622 ; 1st edition (April 5, 2002) • Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potentialby Dieter Fensel (Editor), Wolfgang Wahlster (Editor), Henry Lieberman (Editor) MIT Press; ISBN: 0262062321 ; (November 15, 2002) • Data Mining: Concepts and Techniques by Jiawei Han and Micheline Kamber, ISBN: 1-55860-489-8 • 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 Publishers • Further material will be made available through handouts in class and through pointers to relevant Web pages. CS690L - Lecture 1

  4. Motivation • 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. CS690L - Lecture 1

  5. Course Objectives • 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; • 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); • 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. CS690L - Lecture 1

  6. Course Requirements • A research-oriented graduate course, • A substantial portion of the quarter will be devoted to student presentations of techniques and research papers • Students will be expected to select a problem area in distributed computing architecture and prepare an intensive presentation covering the methods and framework commonly employed to address their problem. • A research paper on the application of a particular middleware architecture is also an acceptable topic. CS690L - Lecture 1

  7. Course Requirements • Discussion/Presentation: • 3 – 4 Workshops (one or two presentations/ workshop) • The lecture/discussions are designed to be highly participatory. • Participation will include such activities as group discussions of topics through workshops; discussions with faculty and student groups on topics, research, and/or application problems; • Short presentations on relevant papers and project results; • Critiques of resource material, software, and other things related to distributed component architecture. CS690L - Lecture 1

  8. Course Requirements • Critical Reading/Thinking: • 30 ~ 50 research papers • 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 reference books will be read and discussed. CS690L - Lecture 1

  9. Course Requirements • Analytical Writing: • 2 - 3 technical reports and one research paper • Students are asked to think critically and reason about information presented in the textbooks or papers. For example, a paper assignment might ask how different frameworks we are studying compare, or how existing technology, like the Web will evolve in the context of component software. • This critical evaluation requires that students offer their own understanding of the significance of what students have learned. CS690L - Lecture 1

  10. Course Requirements • Discovery (Self-guided) Learning: • One or two areas (Semantic Web or Data Mining specialized expertise) • The course project will require independent research and programming, and students are expected to be able to demonstrate ability of this kind. CS690L - Lecture 1

  11. 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. CS690L - Lecture 1

  12. Group Projects • Teams of maximum 4 members, development of a component-based system. • The overall assignment will be split into several steps that will be marked individually. • Project 1 : Project proposal • Project 2: System design • Project 3: System implementation(1) • Project 4: System implementation(2) • Project 5: Documentation & final package CS690L - Lecture 1

  13. Group Projects “Intelligent Knowledge Discovery Tool for Semantic Web” • Building system followed by component-based design and programming (Pluggable components) • Hierarchical Management Structure (Chief Architect, Intermediate Architect, Subgroups, etc) • Incremental outcomes going through Component-based software development process, such as requirement analysis, design, implementation, testing, and integration. • Object-Oriented Specification/Design (UML/Together), Design patterns, styles, Object Framework building using XML, Web Services or .NET. CS690L - Lecture 1

  14. Research Paper • Goal: A research paper (Individual work) • Tentative paper topics: • Paper 1: Semantic Web • Paper 2: Knowledge Discovery (Data Mining, Machine Learning, etc) • Paper 3: Knowledge Discovery for Semantic Web CS690L - Lecture 1

  15. Presentation & Discussion Each student will be participated in three types of presentations & discussion • Workshop • Paper • Project • Small group presentation/discussion CS690L - Lecture 1

  16. Contents of Lecture Workshop 1: Semantic Web • Ontology and Ontology maintenance, • Interoperability, Integration, Composition • Web Services • Representation • XML and XML Schema • RDF and RDF Schema • DAML+OIL, DAML-S • Others (OWL, SHOE, RuleML, etc) CS690L - Lecture 1

  17. Contents of Lecture Workshop 2: Knowledge Discovery • Data Mining and Machine Learning: Prediction, Decision Supporting and Knowledge Discovery • Approaches • Classification • Association • Clustering • Web Mining • Statistical pattern recognition, neural networks, Bayesian learning, genetic algorithms CS690L - Lecture 1

  18. Contents of Lecture Workshop 3: Knowledge Discovery in Semantic Web • Representation • Algorithms • Tools • Killer Application CS690L - Lecture 1

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