Date post: | 11-Apr-2017 |
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Designing a Thesaurus-based Comparison Search Interface forLinked Cultural Heritage Sources
Alia Amin, Michiel Hildebrand,
Jacco van Ossenbruggen, Lynda Hardman
Background
The MultimediaN E-Culture Project
Support cultural heritage experts’information seeking needs
Data
heterogeneous
structured and unstructured
text and images
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Cultural Heritage Experts Information Seeking Tasks*
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Most Experts Information Seeking Tasks are complex
information gathering tasks
e.g. Comparison, Relationship,
Topic search, Exploration,
Combination
Experts search in
multiple sources
* Amin et al., JCDL 2008
Research Goal and Approach
Research goal: support comparison search in multiple sources.
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User
(Curators,
Art historians)
Identify Needs
Design requirementsPreliminary Study
Research Goal and Approach
Research goal: support comparison search in multiple sources.
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User
(Curators,
Art historians) Design & implementation
Identify Needs
Design requirements
Evaluate solutionEvaluation Study
Preliminary Study
Preliminary Study
Goal: to understand comparison search practice performed by CH experts and explore support for comparison search across multiple sources.
7 Experts (curators, art historians)
Semi-structured interview, natural environment, voice recording
Gather comparison search use cases
Get feedback on initial application ideas
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Preliminary Study: Key Findings
When do experts conduct comparison search?
Quantitative and qualitative comparisons
Learning about collections
Planning an exhibition
Museometry
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Preliminary Study: Key Findings
Main challenges in comparison search
Search
Name aliases
Multiple languages
Multiple terms
Compare
…idem
Comparing many sets
Single and multiple property comparison
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Design Requirements
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Search
Need guided search to support:
name aliases, multiple languages, multiple terms
Select
Need to be able to select and group multiple artworks
Compare
Comparing many sets
Single and multiple property comparison
Design and Implementation
• Thesaurus-based comparison search: LISA
• Web platform: ClioPatria
• Interface: HTML, CSS, Javascript, Flash (amChart)
• Dataset
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Collection
RKD Archive 82.781 Objects
Thesauri
RKD Thesaurus 11.995 terms
TGN (geographical) 89.000 terms
ULAN (artist) 13.000 people
AAT (art and architecture) 31.000 terms
IconClass (iconographic) 24.331 terms
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Evaluation Study
Goal: to evaluate how well the search, select and compare features support comparison search tasks
12 CH experts: researchers, curators, librarians, museum managers
Setup Compare LISA vs. baseline (RKDimages)
Introduction
User experiment
Post experiment interview19
Evaluation Study
14 comparison tasks/participant
Compare all paintings from the museum Stedelijk Museum De Lakenhal with
all paintings from the museum Bredius
(1) how many artworks were created in 1650? (single property comparison)
(2) how many artworks were created in 1830 by the artist Jacobus Ludovicus Cornet? (dual properties comparison)
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Tested different comparison tasks:few artworks (2) v.s. many artworks (30) single property v.s. dual propertiesTable, bar chart and scatterplot visualization
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Evaluation Study Results: Search
Search and selection activities are highly interdependent
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Time (t) to search and select t Lisa – few ≈ t Lisa – many
t baseline – few < t baseline – many
Ease of Use (EoU)
EoU baseline-few < EoU Lisa-few
EoU baseline-many < EoU Lisa-many
Evaluation Study Results: Compare
Compare artworks using baseline and Lisa: Table, Barchart, Scatterplot
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Time (t) to compare a Single property
t Lisa -few ≈ t baseline -few
t Lisa – many ≈ t baseline – many
Time (t) to compareDual properties
t Lisa -few ≈ t baseline –few
t Lisa-Scatterplot – many < t Lisa-Table – many <
t baseline – many
Ease of Use (EoU) EoU baseline < EoU Lisa
User Feedback
EOU vs. time.
Autocompletion helped user search for many artworks easier
Different visualizations allow different perspectives
Comparison using a visualization tool is unfamiliar and requires learning time
Additional features requested:
more interactivity with the visualization
Bookmarking 24
Lessons learned
Requirements for the metadata
inconsistent data, incomplete metadata,
estimated data
Tackle through different angles
Data solutions: better annotations
Technical solutions: thesauri alignment, semantic backend, automatically enrich metadata
Interface solutions: transparency on aggregation rules, allow feedback
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Acknowledgements
Centraal Museum Utrecht
Digital Heritage Netherland (DEN)
Efgoed Nederland
Netherlands Collection Institute (ICN)
Publiek Archief Eemland
Netherlands Institute for Art History (RKD)
Rijksmuseum Amsterdam
Tropenmuseum
University of Amsterdam
Hyowon Lee, DCU26
http://e-culture.multimedian.nl/lisa/session/compsearch