Designing a Thesaurus-based Comparison Search Interface forLinked Cultural Heritage Sources

Alia Amin, Michiel Hildebrand,

Jacco van Ossenbruggen, Lynda Hardman

Firstname.lastname@cwi.nl

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