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Sisyphus III

Sisyphus III. Shari Holstege 15 November 2001. Topics. The Myth of Sisyphus Sisyphus Problems Modeling real data Formal Concept Analysis. The Myth of Sisyphus.

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Sisyphus III

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  1. Sisyphus III Shari Holstege 15 November 2001

  2. Topics • The Myth of Sisyphus • Sisyphus Problems • Modeling real data • Formal Concept Analysis

  3. The Myth of Sisyphus Sisyphus was the king of Corinth, or Ephyra. He tricked Hades, the king of the Underworld, several times. As his punishment, he must roll a tremendous boulder up to the top of a hill over and over again, since it rolls back down every time he reaches the summit.

  4. Why Sisyphus Problems? • Early on, developers tried to share knowledge acquisition, modeling, and management tools to evaluate them, but these attempts often failed due to differences in platform and support. • As an alternative, the Sisyphus challenge problems were developed. A knowledge acquisition problem was defined, and developers are challenged to solve it with their own tools.

  5. Overview of the Sisyphus Problems • Room Allocation • Elevator Configuration • Igneous Rock Classification • Integration over the Web

  6. Sisyphus III Objectives • To provide for better quantitative comparison of KB systems and the methodologies employed to build them, through use of a set of achievement metrics • To provide more realistic access to actual KA material in a staged series of releases • To obtain more complete records (or knowledge engineering meta-protocols) concerning the processes that the knowledge engineer goes through in the KBS construction process

  7. Sisyphus III • The project was meant to be a decision support system to assist astronauts to classify rocks and develop a system to tutor them. • The data given the developers was real data from experts and was not familiar to the developers. Their task was to model the data without any additional information.

  8. Problems with Real Data • Different experts have different levels of expertise • Different experts use different terminologies • Transcripts of interviews may be imperfect • Some information is missing • Information my be conflicting

  9. Formal Concept Analysis (FCA) • A mathematical way to find, structure, and display relationships between concepts • A formal context is a triple (G, M, I): • G is a set of objects • M is a set of attributes • I is relation between objects and attributes

  10. An Example - Beverages

  11. Line Diagram

  12. Preparing the Data Resources • Plain scaling – multivalued attributes are replaced by several single-valued attributes At most one of those attributes can be checked in the table • Example – grain size might be divided into coarse, not coarse, and fine, and each of those gets its own column in the table or dot on the line diagram

  13. Grain Size and Emplacement Oops – why is rhyolite all by itself?

  14. Grain Size and Formation Environment Now trachyte is the odd one out.

  15. Grain Size and Color Grain size and color are independent.

  16. Object Model

  17. Converting to the Model • Resolve possible conflicts revealed by the diagram. • If several attributes hold for an identical set of objects, the context can be reduced by deleting all but one of the attributes.

  18. Conclusion • Formal concept analysis is a way to take real data and break it down into a usable format. • It was applied to the Sisyphus-III domain of igneous rock classification to help find noise in the data and to help researchers with little domain knowledge model the data effectively.

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