Exploratory search on topics through different perspectives with DBpedia

Exploratory search on topics through different
perspectives with DBpedia
Nicolas Marie, Fabien Gandon, Alain Giboin, Émilie Palagi
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.

CONTEXT
PROPOSITION
EVALUATION
CONCLUSION
2

CONTEXT
PROPOSITION
EVALUATION
CONCLUSION
3

Search is only a partially solved problem [White, 2009]
Ambiguous queries, natural language queries, exploratory search tasks…

Discovery Hub
10 blue links paradigm,
simple, fast
Exploratory search
bottleneck

Exploratory search systems are optimized to support
exploratory search tasks, common functionalities:
Overviews
Faceted interfaces
Results clustering
Low-cost of browsing (going back-and-forth functionalities)
Query-suggestions and refinement
Serendipitous discoveries provocation
In-session of account related memory features
Discovery Hub

Discovery Hub
Linked data are promising for
supporting exploratory search:
• new algorithms
• new interaction models
optimized for exploration.

Discovery Hub

Discovery Hub Maturity

1 perspective

I want to discover Claude Monet (painter)...
In American culture
Discovery Hub
Topics are complex, multifaceted,
One entity => multiple perspectives &
knowledge nuances
Entourage Art. movement
In French culture Curiosities…

MORE
Aemoo

CONTEXT
PROPOSITION
EVALUATION
CONCLUSION
13

Discovery Hub
The models and algorithms we propose
unveil topic knowledge nuances by allowing the
exploration of topics through several perspectives.
In the graph context of linked data these
perspectives correspond to different non exclusive
sets of objects and relations that are informative on
a topic regarding specific aspects.
Flexible querying and data processing

Building perspectives thanks to spreading activation
……
Refer to the papers for the complete formalization

Discovery Hub
3 perspective-operations to expose
knowledge nuances :
• Criteria of interest specification
• Controlled randomness injection
• Data source selection

Discovery Hub Criteria of interest specification
, dcterms:category, ?x
, dcterms:category, ?x
Classic similarity measure
, dcterms:category, ?a | ?b | ?c |...
, dcterms:category, ?a | ?b | ?c |...
Criteria spec. similarity

Discovery Hub Criteria of interest specification

Classic – top 5 artists
« French / not impressonist » criteria specification – top 5 artists
« Not French / Impressonist » criteria specification – top 5 artists

* r + (1-r)*
Chosen level of randomness
Randomness injection
* r + (1-r)*
* r + (1-r)*
* r + (1-r)*
* r + (1-* r +

Discovery Hub
Local Kgram instance
Data source selection
dbpedia.org/sparql
de.dbpedia.org/sparql
es.dbpedia.org/sparql
fr.dbpedia.org/sparql
it.dbpedia.org/sparql

Discovery Hub Data source selection

CONTEXT
PROPOSITION
EVALUATION
CONCLUSION
28

Evaluated algorithm versions
•Basis algorithm of Discovery Hub
•Personalized algorithm through criteria
specification
•Randomized algorithm, with 0.5 threshold
•Highly randomized algorithm (Highly R.), with
1.0 threshold
Discovery Hub

• Hypothesis 1:
Users who specify their criteria of interest about a topic find the results of
the search more relevant.
• Hypothesis 2:
Users who specify their criteria of interest about a topic do not find the
results of the search less novel.
• Hypothesis 3:
The stronger is the level of randomness the more surprising the results are
for the users.
• Hypothesis 4:
Even if the level of surprise is high, the majority of the top results are still
relevant to the users.
Discovery Hub

Discovery Hub
푃푒푟푠표푛푎푙푖푧푒푑 ; 퐼푛푡푒푟푒푠푡 > [퐵푎푠푖푠 ; 퐼푛푡푒푟푒푠푡]
" ; 퐷푖푠푡푎푛푐푒 < [ " ; 퐷푖푠푡푎푛푐푒]
푃푒푟푠표푛푎푙푖푧푒푑 ; 푆푢푝푟푖푠푖푛푔 푅푒푠푢푙푡 > [퐵푎푠푖푠 ; 푆푢푟푝푟푖푠푖푛푔 푅푒푠푢푙푡]
[ " ; Surprising Relation] > [ " ; 푆푢푟푝푟푖푠푖푛푔 푅푒푙푎푡푖표푛]
퐻3
퐻1
퐻2
퐻푖푔ℎ푙푦 푅. ; 푆푢푝푟푖푠푖푛푔 푅푒푠푢푙푡 > 푅푎푛푑표푚푖푧푒푑 ; 푆푢푟푝푟푖푠푖푛푔 푅푒푠푢푙푡 > [퐵푎푠푖푠 ; 푆푢푟푝푟푖푠푖푛푔 푅푒푠푢푙푡]
[ " ; Suprising Relation] > " ; 푆푢푟푝푟푖푠푖푛푔 푅푒푙푎푡푖표푛 > [ " ; 푆푢푟푝푟푖푠푖푛푔 푅푒푙푎푡푖표푛]
(Highly R. : Highly Randomized)
퐻4
퐻푖푔ℎ푙푦 푅푎푛푑표푚푖푧푒푑 ; 퐼푛푡푒푟푒푠푡 > 퐴푣푒푟푎푔푒 (2,5)
푅푎푛푑표푚푖푧푒푑 ; 퐼푛푡푒푟푒푠푡 > 퐴푣푒푟푎푔푒 (2,5)

A « good »
result in
ESS is…
Our definitions Chosen metrics : Questions
(Likert Scale)
… A
surprising
result
A result is surprising if :
• You discovered an
unknown resource or
relation
• You discovered
something unexpected
Surprising Result
This result is suprising
?
Surprising
Relation
This relation between
the topic searched and
the result is surprising
?
… An
intersting
result
A result is interesting if :
• You think it is similar to
the topic explored
• You think you will
remind or reuse it
Interesting Result
This result is interesting
?
Distance between
the Result and the
topic searched
This result is too distant
from the topic searched
?
Discovery Hub

Discovery Hub
• 16 participants
• Phase 1 : Selection of 2 topics in a list of 20 queries randomly choose in the query log of
Discovery Hub
- Information Visualization
- Serge Gainsbourg (french singer)
• Phase 2 : Specification of the categories of interest
• Phase 3 : User Test (~1h)
- Before the test
- Interview (name, age, do they know Discovery Hub ?,…)
- Presentation of Discovery Hub and the objective of the test
- Presentation of the questions and simulation

H1 : Users who specify their criteria of interest about a topic
find the results of the search more relevant.
퐻1
푃푒푟푠표푛푎푙푖푧푒푑 ; 퐼푛푡푒푟푒푠푡 > [퐵푎푠푖푠 ; 퐼푛푡푒푟푒푠푡]
" ; 퐷푖푠푡푎푛푐푒 < [ " ; 퐷푖푠푡푎푛푐푒]

H2: Users who specify their criteria of interest about a topic do
not find the results of the search less novel
퐻2
푃푒푟푠표푛푎푙푖푧푒푑 ; 푆푢푝푟푖푠푖푛푔 푅푒푠푢푙푡 > [퐵푎푠푖푠 ; 푆푢푟푝푟푖푠푖푛푔 푅푒푠푢푙푡]
[ " ; Surprising Relation] > [ " ; 푆푢푟푝푟푖푠푖푛푔 푅푒푙푎푡푖표푛]

H3: The stronger is the level of randomness the more surprising the results are for the
users
퐻3
퐻푖푔ℎ푙푦 푅. ; 푆푢푝푟푖푠푖푛푔 푅푒푠푢푙푡 > 푅푎푛푑표푚푖푧푒푑 ; 푆푢푟푝푟푖푠푖푛푔 푅푒푠푢푙푡 > [퐵푎푠푖푠 ; 푆푢푟푝푟푖푠푖푛푔 푅푒푠푢푙푡]
[ " ; Suprising Relation] > " ; 푆푢푟푝푟푖푠푖푛푔 푅푒푙푎푡푖표푛 > [ " ; 푆푢푟푝푟푖푠푖푛푔 푅푒푙푎푡푖표푛]
(Highly R. : Highly Randomized)

H4: Even if the level of surprise is high, the majority of the
top results are still relevant to the users
퐻4
퐻푖푔ℎ푙푦 푅푎푛푑표푚푖푧푒푑 ; 퐼푛푡푒푟푒푠푡 > 퐴푣푒푟푎푔푒 (2,5)
푅푎푛푑표푚푖푧푒푑 ; 퐼푛푡푒푟푒푠푡 > 퐴푣푒푟푎푔푒 (2,5)

CONTEXT
PROPOSITION
EVALUATION
CONCLUSION
38

Discovery Hub
•We proposed a framework to enable multi-perspective
exploratory search:
- Formalization
- Implementation
- Evaluation
• 3 operators : criteria spec., randomization, data selection
• Evaluations globally positive, slight adjustements needed
• Interesting propositions from the reviewers, thank you

Thank you ! Questions ?
http://semreco.inria.fr
werarediscoveryhub@gmail.com
Discovery Hub

Exploratory search on topics through different perspectives with DBpedia

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