11th International Conference on Information and Knowledge Management (CIKM'02)
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Tutorials Program
Monday, November 4, 2002
Tutorial 1: Knowledge Management: Principles and Practice
Instructor: Dr. Mark Maybury Room: TBA
Knowledge management (KM) promises to improve the quality, efficiency, and effectiveness of business, education, and government. This tutorial will define terms, outline the history, describe key subfields, and exemplify the field of KM. The tutorial will introduce KM strategy and processes, KM benchmarking, and technologies to support knowledge discovery, dissemination, and expertise management. The tutorial lasts three hours and is primarily a lecture presentation. An on-line version of the tutorial will be made accessible from: http://www.mitre.org/resources/centers/it/maybury/mark.html
Target Audience
This tutorial is intended for students, researchers and practitioners interested in investigating, designing, and/or implementing knowledge management strategy, processes, and systems. There is no prerequisite knowledge required, although general knowledge of management and information technology will enhance the value of this course for participants.
Instructor's Short Biography
Mark Maybury received his M.Phil. in Computer Speech and Language Processing (1987), an MBA from RPI (1989), and his Ph.D. in Artificial Intelligence (1991) for his dissertation, “Generating Multisentential Text using Communicative Acts” at Cambridge University. Mark has organised international symposia, given tutorials, and published over fifty articles in the area of language generation, multimedia presentation, text summarization, and intelligent information retrieval.  Mark is editor of Intelligent Multimedia Interfaces (AAAI/MIT Press, 1993), Intelligent Multimedia Information Retrieval (AAAI/MIT Press, 1997) and co-editor of Readings on Intelligent User Interfaces (Morgan Kaufmann Press, 1998), Advances in Text Summarization (MIT Press, 1999) and Advances in Knowledge Management: Classic and Contemporary Works (MIT Press, 2001) and co-author of Information Storage and Retrieval:  Theory and Implementation. 2nd Edition (Kluwer Academic, 2000) and co-editor of Knowledge Management (MIT Press 2000). Mark is Executive Director of MITRE’s Information Technology Division and a member of the Steering and Program Committees for ACM IUI.
Tutorial 3: Link Analysis: Current State of the Art
Instructor: Dr. Ronen Feldman Room: TBA
The information age has made it easy to store large amounts of data. The proliferation of documents available on the Web, on corporate intranets, on news wires, and elsewhere is overwhelming. However, while the amount of data available to us is constantly increasing, our ability to absorb and process this information remains constant. Search engines only exacerbate the problem by making more and more documents available in a matter of a few key strokes. Link Analysis is a new and exciting research area that tries to solve the information overload problem by using techniques from data mining, machine learning, Information Extraction, Text Categorization, Visualization and Knowledge Management. Link Analysis is the process of building up networks of interconnected objects through various relationships in order to discover patterns and trends. The main tasks of link analysis are to extract, discover, and link together sparse evidence from vast amounts of data sources, to represent and evaluate the significance of the related evidence, and to learn patterns to guide the extraction, discovery, and linkage of entities. The relationships could be transactional, geographical, social, or temporal. Link Analysis involves the preprocessing of document collections (text categorization, term extraction, and information extraction), integration with structured information sources, the storage of the intermediate representations, the techniques to analyze these intermediate representations (distribution analysis, clustering, trend analysis, association rules, etc.) and visualization of the results. In this tutorial we will present the general theory of Link Analysis and will demonstrate several systems that use these principles to enable interactive exploration of a combination of structured and unstructured collections. We will present a general architecture of link analysis systems and will outline the algorithms and data structures behind the systems. The Tutorial will cover the state of the art in this rapidly growing area of research. Several real world applications of link analysis will be presented.
Target Audience
The tutorial should be of interest to practitioners from Data Mining, Bio Information, NLP, IR, Knowledge Management and the general AI audience interested in this fast-growing research area.
Instructor's Short Biography
Ronen Feldman is a senior lecturer at the Mathematics and Computer Science Department of Bar-Ilan University in Israel, and the Director of the Data Mining Laboratory. He received his B.Sc. in Math, Physics and Computer Science from the Hebrew University, M.Sc. in Computer Science from Bar-Ilan University, and his Ph.D. in Computer Science from Cornell University in NY. He is the founder and president of ClearForest Corporation, a NY based company specializing in development of text mining tools and applications. He is also an Adjunct Professor at NYU Stern Business School.

End of CIKM 2002 Tutorials