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Mushroom identification application

Mushroom identification application. Katie Dunn Michael Garber-Barron David Molik Han Wang (with special thanks to Nathan Wilson). Agenda. Problem Background Solution Overview Knowledge Encoding Prototype and Mockups Future Directions. Problem Background.

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Mushroom identification application

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  1. Mushroom identification application Katie Dunn Michael Garber-Barron David Molik Han Wang (with special thanks to Nathan Wilson)

  2. Agenda Problem Background Solution Overview Knowledge Encoding Prototype and Mockups Future Directions

  3. Problem Background Nathan Wilson has been a driver for the project Works with the Encyclopedia of Life Mushroom Observer Galaxy Zoo Idea comes from taxonomy “Taxa” Perhaps a Semantic Mushroom Observer? Mushrooms may superficially look the same, but not necessarily physiologically similar organisms Why? Help enthusiasts

  4. Problem Overview Goal “Help mushroom enthusiasts / citizen scientists categorize mushrooms into classes with common macroscopically observable features, and provide mappings from these classes to taxa that exhibit those features” Rewards Reasoning of similarities in features in mushrooms Ability to select mushrooms that have multiple instances of the same feature Governance of terms and ontology

  5. Problem Solution Create a site that allows users to select features to find a mushroom Be able to select features Mushrooms are paired down Help users with contextual questions Similar mushroom suggestions Must later on be able to be updated with new site features

  6. Knowledge to be encoded: Example #1 Pine Spike Species: • Chroogomphus vinicolor • Chroogomphus ochraceus • Chroogomphus rutilus     ...... Features: • Shape - stipitate agaric • Hymenophore type - gilled • Habitat - pine     ...... C. rutilus http://en.wikipedia.org/wiki/Pine_spike

  7. Knowledge to be encoded: Example #2 Russula • Over 700 species • Fairly large • Brightly colored • Easy to identify the genus, but difficult to distinguish member species • Phylogenetic relationships: polyphyletic R. emetica http://en.wikipedia.org/wiki/Russula

  8. Knowledge to be encoded: Example #3 Gasteromycete • Stomach fungi • Spores produced in fruit bodies, rather than outer surfaces • Includes many species not closely related to each other • Puffballs • Earthstars • Stinkhorns • False truffles • Phylogenetic relationships: polyphyletic Lycoperdon perlatum http://en.wikipedia.org/wiki/Gasteromycete Clathrus archeri http://en.wikipedia.org/wiki/Gasteromycete

  9. Knowledge to be encoded: Mushroom Features • Color • Status • Overall shape • Hymenophore type • Pileus shape from side • Spore print • Size from above • Total height • Head height • Substrate attachment • Taste • Habitat 

  10. Knowledge Encoding: Main Classes Main classes

  11. Knowledge Encoding: Species classes Main classes

  12. Knowledge Encoding: Descriptive vernacular classes

  13. Knowledge Encoding: Feature object properties

  14. Knowledge Encoding: Value partition classes (range of feature classes)

  15. Knowledge Encoding: Dependent features and domain restrictions

  16. Knowledge Encoding: Dependent features and domain restrictions

  17. Prototype / Mockups (activity flow)

  18. Prototype / Mockups •  Interface • SPARQL queries

  19. Future directions: Interface additions   User Input for additional Features and Values   Multiple Value Selections   Full Queries from ontology

  20. Future directions: User-suggested changes to ontology and provenance tracking • Adding features • Community discussion • Tracking provenance

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