Exploratory search on topics through different perspectives with DBpedia
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COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Exploratory search on topics through differentperspectives with DBpedia
Nicolas Marie, Fabien Gandon, Alain Giboin, Émilie Palagi
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CONTEXTPROPOSITIONEVALUATIONCONCLUSION
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3
CONTEXTPROPOSITIONEVALUATIONCONCLUSION
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Search is only a partially solved problem [White, 2009]Ambiguous queries, natural language queries, exploratory search tasks…
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10 blue links paradigm, simple, fast
Exploratory searchbottleneck
Discovery Hub
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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
Exploratory search systems are optimized to support exploratory search tasks, common functionalities:
Discovery Hub
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Discovery Hub
Linked data are promising for supporting exploratory search: • new algorithms • new interaction models optimized for exploration.
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Discovery Hub
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MaturityDiscovery Hub
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1 topic of interest => 1 entity => 1 results set to explore
1 perspective
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Discovery HubTopics are complex, multifaceted,
One entity => multiple perspectives &knowledge nuances
Entourage Art. movement
Curiosities…In French culture
In American culture
I want to discover Claude Monet (painter)...
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MORE
Aemoo
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13
CONTEXTPROPOSITIONEVALUATIONCONCLUSION
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Discovery Hub
The models and algorithms we proposeunveil 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
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COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Refer to the papers for the complete formalization
Building perspectives thanks to spreading activation
……
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Discovery Hub
3 perspective-operations to expose knowledge nuances :
• Criteria of interest specification• Controlled randomness injection• Data source selection
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
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
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Discovery Hub Criteria of interest specification
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Classic – top 5 artists
« French / not impressonist » criteria specification – top 5 artists
« Not French / Impressonist » criteria specification – top 5 artists
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Chosen level of randomness
Randomness injection
* r + (1-r)*
* r + (1-r)*
* r + (1-r)*
* r + (1-r)*
* r + (1-r)*
* r + (1
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Discovery Hub
Local Kgram instance
Data source selection
fr.dbpedia.org/sparql
it.dbpedia.org/sparql
de.dbpedia.org/sparql
es.dbpedia.org/sparql
dbpedia.org/sparql
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Discovery HubData source selection
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28
CONTEXTPROPOSITIONEVALUATIONCONCLUSION
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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
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• 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
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
𝐻3 𝐻𝑖𝑔ℎ𝑙𝑦 𝑅. ; 𝑆𝑢𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡 > 𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑧𝑒𝑑 ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡 > [𝐵𝑎𝑠𝑖𝑠 ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡]
[ " ; Suprising Relation] > " ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛 > [ " ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛]
𝐻2 𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑧𝑒𝑑 ; 𝑆𝑢𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡 > [𝐵𝑎𝑠𝑖𝑠 ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡]
[ " ; Surprising Relation] > [ " ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛]
𝐻1 𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑧𝑒𝑑 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 > [𝐵𝑎𝑠𝑖𝑠 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡]
" ; 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 < [ " ; 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒]
𝐻4 𝐻𝑖𝑔ℎ𝑙𝑦 𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑧𝑒𝑑 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 > 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 (2,5)
𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑧𝑒𝑑 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 > 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 (2,5)
(Highly R. : Highly Randomized)
Discovery Hub
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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
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• 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
Discovery Hub
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𝐻1 𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑧𝑒𝑑 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 > [𝐵𝑎𝑠𝑖𝑠 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡]
" ; 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 < [ " ; 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒]
H1 : Users who specify their criteria of interest about a topic find the results of the search more relevant.
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
𝐻2 𝑃𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑖𝑧𝑒𝑑 ; 𝑆𝑢𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡 > [𝐵𝑎𝑠𝑖𝑠 ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡]
[ " ; Surprising Relation] > [ " ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛]
H2: Users who specify their criteria of interest about a topic do not find the results of the search less novel
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𝐻3 𝐻𝑖𝑔ℎ𝑙𝑦 𝑅. ; 𝑆𝑢𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡 > 𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑧𝑒𝑑 ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡 > [𝐵𝑎𝑠𝑖𝑠 ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑠𝑢𝑙𝑡]
[ " ; Suprising Relation] > " ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛 > [ " ; 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑖𝑛𝑔 𝑅𝑒𝑙𝑎𝑡𝑖𝑜𝑛]
(Highly R. : Highly Randomized)
H3: The stronger is the level of randomness the more surprising the results are for the users
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
𝐻4 𝐻𝑖𝑔ℎ𝑙𝑦 𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑧𝑒𝑑 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 > 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 (2,5)
𝑅𝑎𝑛𝑑𝑜𝑚𝑖𝑧𝑒𝑑 ; 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 > 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 (2,5)
H4: Even if the level of surprise is high, the majority of the top results are still relevant to the users
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38
CONTEXTPROPOSITIONEVALUATIONCONCLUSION
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
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
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http://semreco.inria.fr
Thank you ! Questions ?
Discovery Hub