Mushroom identification application

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

description

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. - PowerPoint PPT Presentation

Transcript of Mushroom identification application

Page 1: Mushroom identification application

Mushroom identification application

Katie DunnMichael Garber-Barron

David MolikHan Wang

 (with special thanks to Nathan Wilson)

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Agenda

1. Problem 1.Background2.Solution Overview

2. Knowledge Encoding3. Prototype and Mockups4. Future Directions

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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 organismsWhy? Help enthusiasts

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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

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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

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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. rutilushttp://en.wikipedia.org/wiki/Pine_spike

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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. emeticahttp://en.wikipedia.org/wiki/Russula

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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 othero Puffballso Earthstarso Stinkhornso False truffles

• Phylogenetic relationships: polyphyletic

Lycoperdon perlatumhttp://en.wikipedia.org/wiki/Gasteromycete

Clathrus archerihttp://en.wikipedia.org/wiki/Gasteromycete

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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 

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Knowledge Encoding: Main Classes

Main classes

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Knowledge Encoding: Species classes

Main classes

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Knowledge Encoding: Descriptive vernacular classes

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Knowledge Encoding: Feature object properties

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

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Knowledge Encoding: Dependent features and domain restrictions

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Knowledge Encoding: Dependent features and domain restrictions

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Prototype / Mockups

(activity flow)

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Prototype / Mockups

 •  Interface

 • SPARQL queries

 

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Future directions: Interface additions

   User Input for additional Features and Values

  Multiple Value Selections     Full Queries from ontology

 

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Future directions: User-suggested changes to ontology and provenance tracking

• Adding features • Community discussion

 • Tracking provenance