Constellation: A Visualization Tool for Linguistic Queries from MindNet

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. Semantic Layout • reflect dataset characteristics • path ordering as backbone • fill in definition graphs

11. Semantic Layout • “plausibility gradient”

12. Semantic Layout • “plausibility gradient” • horizontal position

13. Semantic Layout • “plausibility gradient” • horizontal position • size

14. Semantic Layout • edge crossings not minimized

15. Semantic Layout • edge crossings not minimized • false attachment solved with interactive selective emphasis

16. Perceptual Channels • redundant combinations • synergy from multiple codings • layout gradient • spatial position, word size • quantitative

17. Perceptual Channels • highlighting:visual popout • saturation • brightness • linewidth • ordered • although binary

18. Perceptual Channels • highlighting:visual popout • saturation • brightness • linewidth • ordered • although binary

19. Perceptual Channels • hue • relation types • green: part-of • red: is-a • cyan: modifier • word types • yellow: path • green: definition graph • blue: leaf • selective (nominal)

20. Perceptual Channels • orientation • relation types • axis-aligned: local • slanted: long distance • between instancesof same word • selective (nominal)

21. Perceptual Channels • enclosure • definition graphs associated with path word • hierarchy

22. Interaction • see video

23. Video • zoom • software vs. video

24. 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

27. Semantic Layout Challenges • navigation • intelligent zooming • global • path structure overview • intermediate • association of path word and definition graphs • local • read single definition graph

30. 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

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

32. 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