Cultural Heritage domain has opened up to contributions from the users on the web. The contributions are mainly in the form of tags which describe certain aspect of the cultural heritage object. With a wide range of users on the web, it becomes important to determine the quality of the user contributed content before it is published online. However, manually evaluating the quality of these user generated contributions is exhausting in terms of resources for the Cultural Heritage institutions. In this talk, I will describe methods which can semi-automatically predict the quality of tags. These methods address three research questions: How can we trust an online contributor?, How can we assess the quality of annotation process?, How can we trust the contributed data?.
1. Trusting user-contributed data in
Cultural Heritage Domain
Archana Nottamkandath
(Work done with Davide Ceolin & Wan Fokkink)
VU University Amsterdam
COMMIT/SEALINC
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7. Evaluation costs Resources
• Is expensive manual labor
• Costs a lot of time
• Requires adherence to museum policies
– Museum X [Accept, not sure, reject]
– Museum Y [Foreign, Judgmental, Strong reject,
Strong accept ]..
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8. Need for automated trust analysis
• Algorithms automatically/ semi-automatically
evaluate annotations
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(a) Flower
(b) 19th
century
(c) Sunshine
(d) Vermeer
(e) Bronze
9. Automated Trust analysis algorithms
• Requirements
– High accuracy (Accurately predict evaluations
most of the time)
– Minimum input from cultural heritage
professionals
– Scalable and Efficient (w.r.t resources and time)
– Works with different cultural heritage data
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10. Definition
• Trustworthy annotation
– Relevant to image
– Enhances/re-instates existing knowledge
– Is acceptable by museums policies to be published
on their website
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12. Integrate Trust to Existing workflow
(Research Question1)
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Tulips
Roses
Night Sky
Van Gogh
Buddhist
Portrait
Monument
Asian
War
memorial
User_name: Jones
contributed
Used
Accurator Interface
Tags
RQ1:How to determine trust from user contributing
annotations to the system?
13. Integrate Trust to Existing workflow
(Research Question 2)
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Tulips
Roses
Night Sky
Van Gogh
Buddhist
Portrait
Monument
Asian
War
memorial
User_name: Jones
contributed
Used
Accurator Interface
Tags
RQ2: How to determine trust from the Annotation Process?
14. Integrate Trust to Existing workflow
(Research Question 3)
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Tulips
Roses
Night Sky
Van Gogh
Buddhist
Portrait
Monument
Asian
War
memorial
User_name: Jones
contributed
Used
Accurator Interface
Tags
RQ3: How to determine trust from contributed data?
15. RQ1:Determine trust from users[1]
• Evaluate subset of user tags
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Tulips
Roses
Night Sky
Van Gogh
Buddhist
Portrait
Monument
Asian
War
memorial
User_name: Jones Test set
Roses
Night sky
Van Gogh
Asian
War
Memorial
contributed
Train set
Tulips
Van Gogh
Buddhist
Monument
Evaluates
Museum
16. • User expert on one topic might be expert on
similar topics
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Expert on
Tulips
Possibly
Expert on
Possibly
Expert on
Roses
Lilies
User_name: Jones
Test set
Roses
Night sky
Van Gogh
Asian
War
Memorial
Train
setTulips
Van Gogh
Buddhist
Monument
RQ1:Determine trust from users[1]
With a certain probability
17. RQ1:Determine trust from users[2]
• User profile : [Experience, education, country,
gender, income, museum visits…]
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Steve.museum
dataset
18. RQ1:Determine trust from users[2]
• Predict user reputation using Support Vector
Machines(SVM)
• [Feature1, Feature2, ..] -> Category of user
– [21 yrs, Female, Bachelors, Australia] -> Excellent
– [60 yrs, Male, PhD, America] -> Good
– [56 yrs, Female, Masters, Croatia] -> Bad
– [30 yrs, Male, High School, Mexico] -> ?
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19. RQ2: Determine trust from Annotation
process
• Time of day, Day of week, Day of month etc.
affect user quality
• Typing speed affects user quality
– Typing fast might indicate higher confidence
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Tulips
Van Gogh
Buddhist
Monument
Rich Lady
Plant
Leonardo
Bronze plate
20. RQ2: Determine trust from Annotation
process
• Predict tag quality using Support Vector
Machines(SVM)
• [Feature1, Feature2, ....] -> Category of Tag
– [10:00, Monday, June, 3s] -> Excellent
– [12:00, Wednesday, 15s] -> Good
– [23:56, Friday, April, 80s] -> Bad
– [06:00, Thursday, March, 70s] -> ?
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21. RQ2: Determine trust from Annotation
process
• Why is this important?
– Useful for anonymous users who did not fill profile
information
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22. RQ3: Determine trust from data
• Contributed data itself has features, train SVM
on features to predict quality of tag
– Length
– Specificity
– Presence in vocabularies
– Times already contributed
– Noun
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Tulips
Van Gogh
Buddhist
Monument
[6,specific, yes, English, 10, no…] -> Good
[7,specific, yes, Dutch, 1,yes…] -> Bad
23. Goals achieved
• Requirements
– High accuracy (Accurately predict evaluations
most of the time)
– Minimum input from cultural heritage
professionals
– Scalable and efficient
– Works with different cultural heritage data
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25. – High accuracy (Accurately predict evaluations
most of the time)
• Predicted quality of a tag based on user profile with
accuracy from 68% to 72%
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Steve dataset results
Goal 1: High Accuracy
26. Goal 2: Minimum input from
Cultural Heritage Institutions
• Algorithms require minimum of 5 evaluated
tags per user for predictions
• Working on to minimize/eliminate this
requirement
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27. Goal 3: Scalable and efficient
• Reduced computation time while maintaining
accuracy in Steve dataset
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28. Goal 4: Works with different
cultural heritage data
• Steve Museum dataset
• Waisda? Dataset
– Video Tagging Game
• SEALINC Media experiments at CWI
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29. Future Work
• Employ our experiences and algorithms to
analyze the data from Accurator
• Employ trust scores for ranking in search
• Identify techniques to visualize trust
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