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Constellation: A Visualization Tool for Linguistic Queries from MindNet

Constellation is a visualization tool designed to help linguists improve MindNet algorithms through custom semantic layouts and interactive exploration. It enables analyzing relationships between words, senses, and relation types in large semantic networks. By offering features like Path Query for finding N paths between words and Plausibility Checking for ordering paths by believability, Constellation aids in algorithm debugging and analysis. The tool also addresses challenges related to spatial layout, color schemes, and interaction design. Developed by a collaborative team from Stanford University and Microsoft Research, Constellation leverages spatial and perceptual channels for effective data exploration and offers a broadly applicable approach.

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Constellation: A Visualization Tool for Linguistic Queries from MindNet

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  1. Constellation: A Visualization Tool for Linguistic Queries from MindNet Tamara Munzner François Guimbretière Stanford University George Robertson Microsoft Research

  2. Overview • solve specific problem • help linguists improve MindNet algorithms • chosen techniques • custom semantic layout • perceptual channels • interaction as first-class citizen

  3. Definition Graph • dictionary entry sentence • nodes: word senses • links: relation types

  4. Semantic Network • definition graphs as building blocks • unify shared words • large network • millions of nodes • global structure known: dense • probes return local info • uses • grammar checking, automatic translation

  5. Path Query • best N paths between two words • words on path itself • definition graphs used in computation

  6. Task: Plausibility Checking • paths ordered by computed plausibility • researcher hand-checks results • high-ranking paths believable? • believable paths high-ranked? • gross polluters (stop words)

  7. Top 10 Paths: kangaroo - tail

  8. Top 10 Paths: kangaroo - tail

  9. Goal • create unified view of relationships between paths and definition graphs • shared words are key • thousands of words (not millions) • special-purpose algorithm debugging tool • not understand the structure of English

  10. Video • zoom • software vs. video

  11. Semantic Layout Challenges • spatial position encodes path ordering • edge crossings not minimized • clutter reduction:interaction, perceptual channels • tradeoffs • spatial encoding vs. information density • navigation: intelligent zooming • global, intermediate, local

  12. Color Scheme [Reynolds94] • hues • maximally separated on color wheel • saturation/brightness • low for unobtrusive, high for emphasis • maximal CRT legibility • black text on colored background

  13. Conclusion • targeted case study • small user community • techniques • encode dataset structure spatially • multiple perceptual channels • interactive selective emphasis, navigation • approach broadly applicable

  14. Acknowledgements • MSR linguists • Lucy Vanderwende, Bill Dolan, Mo Corston-Oliver • iterative design techniques • Mary Czerwinski • discussion • Maneesh Agrawala, Pat Hanrahan, Chris Stolte, Terry Winograd • funding • Microsoft Graduate Research Fellowship, Interval Research • http://graphics.stanford.edu/papers/const • http://graphics.stanford.edu/~munzner/talks/vis99

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