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Linked data based exploratory searchPhd defense – Nicolas MARIE – 12.12.2014
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10 blue links paradigm, Simple, fast
Exploratory searchbottleneck
Exploratory search: open search tasks having a general objective like learning or investigating a poorly known topic
Search is only a partially solvedproblem
[White, 2009]
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10 blue links paradigm, Simple, fast
Exploratory searchbottleneck
Exploratory search: open search tasks having a general objective like learning or investigating a poorly known topic
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10 blue links paradigm, Simple, fast
Exploratory searchbottleneck
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<1990 Web
<2001 + Semanticweb
<2007+ Linked data
<2012 +ProprietaryKG
Semantic search refers to the incorporation of structured semantics in search
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HumanComputer InformationRetrieval
Linked data based exploratory searchResearch questions
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HumanComputer InformationRetrieval
Linked data based exploratory searchResearch questions
• How can we discover and rank linked resources to be explored starting from the user topic(s) of interest?
• How to address remote linked data source for this selection?
• How to optimize a data based exploration at the interaction level?
• How to evaluate such exploratory search systems?
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Plan
• How can we discover and rank linked resources to be explored starting from the user topic(s) of interest?
• How to address remote linked data source for this selection?
• How to optimize such data based exploration approach at the interaction level?
• How to evaluate such exploratory search systems?
• State-of-the-art review
• Conclusion
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Plan
• How can we discover and rank linked resources to be explored starting from the user topic(s) of interest?
• How to address remote linked data source for this selection?
• How to optimize such data based exploration approach at the interaction level?
• How to evaluate such exploratory search systems?
• State-of-the-art review
• Conclusion
• Contribution 1: several exploratory search algorithms optimized for linked datasets
• Contribution 2: their efficient execution over distant linked datasets
• Contribution 3: a novel interaction model and its implementation in the Discovery Hub web application
• Contribution 4: multiple users’ evaluations of the Discovery Hub results
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Plan
• How can we discover and rank linked resources to be explored starting from the user topic(s) of interest?
• How to address remote linked data source for this selection?
• How to optimize such data based exploration approach at the interaction level?
• How to evaluate such exploratory search systems?
• State-of-the-art review
• Conclusion
• Contribution 1: several exploratory search algorithms optimized for linked datasets
• Contribution 2: their efficient execution over distant linked datasets
• Contribution 3: a novel interaction model and its implementation in the Discovery Hub web application
• Contribution 4: multiple users’ evaluations of the Discovery Hub results
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State-of-the-art review
Linked data• Browsers• Recommenders• Exploratory search systems
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State-of-the-art review
Linked data• Browsers• Recommenders• Exploratory search systems
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State-of-the-art review
Linked data• Browsers• Recommenders• Exploratory search systems
Trade-off
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Systems matrix
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Timeline
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Opportunities identification
Semantic search aspects
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Opportunities identification
Exploratory search aspects
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Plan
• How can we discover and rank linked resources to be explored starting from the user topic(s) of interest?
• How to address remote linked data source for this selection?
• How to optimize such data based exploration approach at the interaction level?
• How to evaluate such exploratory search systems?
• State-of-the-art review
• Conclusion
• Contribution 1: several exploratory search algorithms optimized for linked datasets
• Contribution 2: their innovative implementation over linked datasets
• Contribution 3: a novel interaction model and its application in the Discovery Hub web application
• Contribution 4: multiple users’ evaluations of the Discovery Hub results
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Algorithm - challenges
• Richness/complexity of linked datasets• Supporting diverse exploration needs
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Spreading activation
• Well-tried algorithm• Connectionist• Semantic friendly• Fast
1.0
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Semantic spreading activation
Filtering
Similarity
Semanticweighting
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a
w
g
q
j
k
s m
i
p
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o
b
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1
class propagation domain:
Artist, Museum
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a
w
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q
j
k
s m
i
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1
class propagation domain:
Artist, Museum
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a
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qs m
p
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1
, dcterms:category, ?x
, dcterms:category, ?x
Similarity computation
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a
w
g
qs m
p
e
o
n
1
a(...)a(...) * 4
a(...)
a(...)
a(...) * 3
a(...)
a(...)a(...)
a(...)
a(...) * 2
a(...) * 3
, dcterms:category, ?x
, dcterms:category, ?x
Category:People from ParisCategory:French paintersCategory:Impressionist painters
= +3
Similarity computation
a(...)
a(...)
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Composites interest queries: knowing my interest for X and Y what can I discover/learn which is related to all theseresources?
The BeatlesKen Loach
Polycentric algorithm
?????
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Composites interest queries: knowing my interest for X and Y what can I discover/learn which is related to all theseresources?
Ken Loach
Polycentric algorithm
Margaret Thatcher
?????
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Polycentric semantic spreading activationPropagation domain ( ) Propagation domain ( , )
Bands, Movies, Music genreMovies, Office Holder,
Political party
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Multi-perspectives exploration
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 perspectivescorrespond 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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2 perspective-operations to expose the topic knowledge nuances • Criteria of interest specification• Controlled randomness injection
Multi-perspectives exploration
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, dcterms:category, ?x
, dcterms:category, ?x
Classic similarity measure
, dcterms:category, ?a | ?b | ?c |...
, dcterms:category, ?a | ?b | ?c |...
Criteria spec. similarity
Multi-perspectives
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, dcterms:category, ?x
, dcterms:category, ?x
Classic similarity measure
, dcterms:category, ?a | ?b | ?c |...
, dcterms:category, ?a | ?b | ?c |...
Criteria spec. similarity
Multi-perspectives
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Chosen level of randomness
Multi-perspectives
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Chosen level of randomness
Multi-perspectives
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Plan
• How can we discover and rank linked resources to be explored starting from the user topic(s) of interest?
• How to address remote linked data source for this selection?
• How to optimize such data based exploration approach at the interaction level?
• How to evaluate such exploratory search systems?
• State-of-the-art review
• Conclusion
• Contribution 1: several exploratory search algorithms optimized for linked datasets
• Contribution 2: their efficient execution over distant linked datasets
• Contribution 3: a novel interaction model and its application in the Discovery Hub web application
• Contribution 4: multiple users’ evaluations of the Discovery Hub results
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• Computing the results from distant datasets• Computing the results on run-time
Algorithm execution - challenge
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Remote LOD sourceComputation at query-timefrom distant linked datasets
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• Dbpedia 3.7• 3.4 million resources• 270 million triples
• Spread in both direction• Categories based similarity
Settings
Mirizzi and al., 2010
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Import size
Result quality Iterations
Trade-off
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Algorithm studied on 100.000 representativeDBpedia queries obtained with a random walker
J. Leskovec and C. Faloutsos. Sampling from large graphs, 2006
Algorithm behavior analysis
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Analysis - Number of iterations
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Analysis - Number of iterations
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Analysis – Triples imported
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Analysis – Triples imported
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Remote LOD sourceComputation at query-time
?
? ?
??
Analysis – Convergence polycentric
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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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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 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
Analysis: 700 queries from the Discovery Hub
query-log
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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
Analysis: 700 queries from the Discovery Hub
query-log
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Analysis on random graphs
Monocentric algorithm behavior analyzed on 3700+ random graphs having diverse characteristics
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Analysis on random graphs
Monocentric algorithm behavior analyzed on 3700+ random graphs having diverse characteristics
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Plan
• How can we discover and rank linked resources to be explored starting from the user topic(s) of interest?
• How to address remote linked data source for this selection?
• How to optimize such data based exploration approach at the interaction level?
• How to evaluate such exploratory search systems?
• State-of-the-art review
• Conclusion
• Contribution 1: several exploratory search algorithms optimized for linked datasets
• Contribution 2: their efficient execution over distant linked datasets
• Contribution 3: a novel interaction model and its application in the Discovery Hub web application
• Contribution 4: multiple users’ evaluations of the Discovery Hub results
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• Build an intuitive and supportive interface• Favor the users engagement
Interactions - challenge
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Application design http://discoveryhub.co
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Application design http://discoveryhub.co
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http://discoveryhub.co
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Examples of evolutions
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Examples of evolutions
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Favoris
Nouvelle recherche TEMPS
Debut test Free Jazz 24s
Free improvisation 33s
(fiche) Avant-garde 47s
John Coltrane (vidéo) 1min 28
Marc Ribot 2min11
(fiche) experimental music 2min18 2min23
Krautrock 2min31
(fiche) Progressive rock 2min37 2min39
Red (King Crimson album) 2m52 2min59
King Crimson 3min05
(fiche) Jazz fusion 3min18
(fiche) Free Jazz 3min32 3min54
Sun Ra 4min18
(fiche) Hard bop 4min41 4min47
Charles Mingus (vidéo) 5min29
(fiche) Third Stream (vidéo) 6min20
Bebop 7min19
Modal jazz 7min26
(fiche) Saxophone 7min51 7min55
Mel Collins
21st Century Schizoid Band
Crimson Jazz Trio
(fiche)King Crimson
(fiche)Robert Fripp
Miles Davis
Thelonious Monk
(fiche) Blue Note Record
McCoy Tyner
(fiche) Modal Jazz
(fiche) Jazz
Chick Corea
(fiche) Jazz Fusion
Return to Forever
Mahavishnu Orchestra
Shakti (band)
U.Srinivas
Bela Fleck
Flecktones
John McLaughlin (musician)
Dixie Dregs
FICHE Dixie Degs
T Lavitz
Jordan Rudess
Behold… The Arctopus
(fiche) Avant-garde metal
Unexpected
FICHE unexpected
Dream Theater
King Crimson
(fiche) Jazz fusion
King Crimson
Tony Levin
(fiche) Anderson Bruford Wakeman Howe
(fiche) Rike Wakeman (vidéo)
Fin test
Ongoing work – Palagi, Giboin and al.
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Plan
• How can we discover and rank linked resources to be explored starting from the user topic(s) of interest?
• How to address remote linked data source for this selection?
• How to optimize such data based exploration approach at the interaction level?
• How to evaluate such exploratory search systems?
• State-of-the-art review
• Conclusion
• Contribution 1: several exploratory search algorithms optimized for linked datasets
• Contribution 2: their efficient execution over distant linked datasets
• Contribution 3: a novel interaction model and its application in the Discovery Hub web application
• Contribution 4: multiple users’ evaluations of the Discovery Hub results
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• Designing exploratory search evaluation protocols• Evaluating all the Discovery Hub algorithms
Evaluation - challenge
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Using
Cases
Compared to sSVM [Mirizzi and al., 2010]
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Hypotheses
1. The MSSA algorithm gives results at least as relevant as the sVSM one, even if it is not domain-optimized
2. The MSSA algorithm has less degradation than the sVSM algorithm
3. There is a greater chance that the results are less relevant but newer to
users at the end of the lists.
15 participants, questions
1. With the film A, I think I will live a similar cinematographic experience as with the film B ? Strongly agree, agree, disagree, strongly disagree
2. You and the film A ? Seen, known but not seen, unknown
Monocentric evaluations
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Very relevant
Not relevant at all
Monocentric evaluations
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Very relevant
Not relevant at all
Hypothesis 1: The SSA algorithm gives results at least as relevant as the sVSM one
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Very relevant
Not relevant at all
Known
Not known
Hypothesis 2: The SSA algorithm has less degradation than the sVSM one.
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Very relevant
Not relevant at all
Known
Not known
Hypothesis 3: There is a greater chance that the results are less relevant but newer to users at the end of the lists.
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Very relevant
Not relevant at all
Hypothesis 4: The explanatory features increase the users’ overall judgments positivity.
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Random combination
Evaluated using
dbpedia:A dbpedia:B dbpedia:C dbpedia:D
= = = =
dbpedia:A & dbpedia:D
Hypothesis 5: the composite-query results
are interesting to the users
Hypothesis 6: a consequent proportion of the results are unexpected; they favor discoveries
Question 1: The result interests me: [Strongly
agree, agree, disagree, strongly disagree].
Question 2: The result is unexpected: [strongly
agree, agree, disagree, strongly disagree]
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•61.6% of the results were rated as strongly relevant or relevant by the participants.
•65% of the results were rated as strongly unexpected or unexpected.
•35.4% of the results were rated both as strongly relevant or relevant and strongly unexpected or unexpected.
Hypothesis 5: the composite-query results are interesting to the users
Hypothesis 6: a consequent proportion of the results are unexpected
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Helpful
Not helpful at all
Hypothesis 7: The explanatory features help the users to understand the results
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Using
Information visualization
Serge Gainsbourg
Algorithm variantscompared:
• Basis• Criteria• Randomized - 0.5• Randomized - 1
Advanced querying evaluation
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Hypotheses
• Hypothesis 8: The users who specify their
criteria of interest find the results of the search
more relevant
• Hypothesis 9: The users who specify their
criteria of interest do not find the results less
novel
• Hypothesis 10: The stronger is the level of
randomness the more surprising the results are
for the users.
• Hypothesis 11: Even if the level of surprise is
high, the majority of the top results are still
relevant to the users.
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Questions
1. Is the result in itself is surprising?
2. Is the relation between the result and the
query is surprising?
3. Is the result interesting?
4. Is the result too distant from the topic
searched?
Very close 1-2-3-4 Too distant.
Not agree at all 1-2-3-4 Totally agree
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COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Plan
• How can we discover and rank linked resources to be explored starting from the user topic(s) of interest?
• How to address remote linked data source for this selection?
• How to optimize such data based exploration approach at the interaction level?
• How to evaluate such exploratory search systems?
• State-of-the-art review
• Conclusion
• Contribution 1: several exploratory search algorithms optimized for linked datasets
• Contribution 2: their efficient implementation over distant linked datasets
• Contribution 3: a novel interaction model and its application in the Discovery Hub web application
• Contribution 4: multiple users’ evaluations of the Discovery Hub results
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• Contribution 1: four exploratory search algorithms leveraging the linked data semantics
Monocentric
Polycentric
Criteria variant
Randomized variant
…
…
…
…
Contribution summary
• How can we discover and rank linked resources to be explored starting from the user topic(s) of interest?
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
• Contribution 1: four exploratory search algorithms leveraging the linked data semantics
Monocentric
Polycentric
Criteria variant
Randomized variant
…
…
…
…
Contribution summary
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• How to address remote linked data source for this selection?
Contribution summary
• Contribution 2: efficient algorithm implementation over distant linked datasets
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• How to optimize such data based exploration approach at the interaction level?
Contribution summary
• Contribution 3: a novel interaction model and its application in the Discovery Hub web application
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• How to evaluate such exploratory search systems?
• Contribution 4: multiple users’ evaluations of the Discovery Hub results
3 user evaluations
Contribution summary
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Short-term improvements
-Facet and collection-levels interactions
-Collaborative exploratory search
-Post-query re-ranking
-New query-means
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Long-term perspectives
- Negative search
- Adaptive queries recommendation
- Collection-based knowledge creation
- On-the-fly distributed data selection
- Massive use and commercial success
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Other achievements
7 other publications in the fields of social networks and web sciences, sciences, including Web Sciences 2012Winner of the Challenge Jeunes Pousses 2013, in teamSeveral interventions during technological events
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Other achievements
7 other publications in the fields of social networks and web sciences, sciences, including Web Sciences 2012Winner of the Challenge Jeunes Pousses 2013, in teamSeveral interventions during technological events
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Other achievements
7 other publications in the fields of social networks and web sciences, sciences, including Web Sciences 2012Winner of the Challenge Jeunes Pousses 2013, in teamSeveral interventions during technological events
COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Other achievements
7 other publications in the fields of social networks and web sciences, sciences, including Web Sciences 2012Winner of the Challenge Jeunes Pousses 2013, in teamSeveral interventions during technological events
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• Nicolas Marie, Fabien Gandon. Demonstration of multi-perspective exploratory searchwith the Discovery Hub web application, ISWC2014, Riva Del Garda, Italy (demo)
• Nicolas Marie, Fabien Gandon. Survey of linked data based exploration systems, IESD2014, Riva Del Garda, Italy (long paper)
• Nicolas Marie, Fabien Gandon, Alain Giboin, Emilie Palagi. Exploratory search on topics through different perspectives with DBpedia, Semantics 2014, Leipzig, Germany (long paper)
• Nicolas Marie, Fabien Gandon, Myriam Ribière, Florentin Rodio. Discovery Hub: on-the-flylinked data exploratory search, I-Semantics 2013, TU Graz, Austria (long paper)
• Nicolas Marie, Fabien Gandon, Damien Legrand, Myriam Ribière. Exploratory search on the top of DBpedia chapters with the Discovery Hub application (demo + poster)
• Nicolas Marie, Olivier Corby, Fabien Gandon, Myriam Ribière. Composite interests’ exploration thanks to on-the-fly linked data spreading activation, Hypertext 2013, Paris (long paper)
16% acceptance rate
ESWC Best demonstration award
Thank you ! Questions ?
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Annexes
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Tomaso Di Noia and al. Linked Open Data to support
Content-based Recommender Systems. I-Semantics
2012
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[Passant, 2010]
[Mirizzi, 2010]
Wateilonis, 2010
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p.223
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p.216
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COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.