Phd defense - Linked data based exploratory search - Nicolas MARIE

COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.

Linked data based exploratory searchPhd defense – Nicolas MARIE – 12.12.2014


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]




Exploratory search: open search tasks having a general objective like learning or investigating a poorly known topic





<1990 Web

<2001 + Semanticweb

<2007+ Linked data

<2012 +ProprietaryKG

Semantic search refers to the incorporation of structured semantics in search


HumanComputer InformationRetrieval

Linked data based exploratory searchResearch questions


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?


Plan



• How to optimize such data based exploration approach at the interaction level?


• State-of-the-art review

• Conclusion


Plan






• 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


Plan






• Conclusion



• Contribution 3: a novel interaction model and its implementation in the Discovery Hub web application



State-of-the-art review

Linked data• Browsers• Recommenders• Exploratory search systems







Trade-off


Systems matrix


Timeline


Opportunities identification

Semantic search aspects


Opportunities identification

Exploratory search aspects


Plan






• Conclusion


• Contribution 2: their innovative implementation over linked datasets

• Contribution 3: a novel interaction model and its application in the Discovery Hub web application



Algorithm - challenges

• Richness/complexity of linked datasets• Supporting diverse exploration needs


Spreading activation

• Well-tried algorithm• Connectionist• Semantic friendly• Fast

1.0





Semantic spreading activation

Filtering

Similarity

Semanticweighting






a

w

g

q

j

k

s m

i

p

e

o

b

n

1

class propagation domain:

Artist, Museum


a

w

g

q

j

k

s m

i

p

e

o

b

n

1

class propagation domain:

Artist, Museum


a

w

g

qs m

p

e

o

n

1

, dcterms:category, ?x


Similarity computation


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



Category:People from ParisCategory:French paintersCategory:Impressionist painters

= +3

Similarity computation

a(...)

a(...)


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

?????


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

?????







Polycentric semantic spreading activationPropagation domain ( ) Propagation domain ( , )

Bands, Movies, Music genreMovies, Office Holder,

Political party


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


2 perspective-operations to expose the topic knowledge nuances • Criteria of interest specification• Controlled randomness injection

Multi-perspectives exploration




Classic similarity measure

, dcterms:category, ?a | ?b | ?c |...


Criteria spec. similarity

Multi-perspectives




Classic similarity measure



Criteria spec. similarity

Multi-perspectives


Chosen level of randomness

Multi-perspectives


Chosen level of randomness

Multi-perspectives


Plan






• Conclusion






• Computing the results from distant datasets• Computing the results on run-time

Algorithm execution - challenge


Remote LOD sourceComputation at query-timefrom distant linked datasets








• Dbpedia 3.7• 3.4 million resources• 270 million triples

• Spread in both direction• Categories based similarity

Settings

Mirizzi and al., 2010


Import size

Result quality Iterations

Trade-off


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


Analysis - Number of iterations


Analysis - Number of iterations


Analysis – Triples imported


Analysis – Triples imported









Remote LOD sourceComputation at query-time

?

? ?

??

Analysis – Convergence polycentric


Classic – top 5 artists

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

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


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


Discovery Hub







dbpedia.org/sparql

Analysis: 700 queries from the Discovery Hub

query-log


Discovery Hub







dbpedia.org/sparql

Analysis: 700 queries from the Discovery Hub

query-log


Analysis on random graphs

Monocentric algorithm behavior analyzed on 3700+ random graphs having diverse characteristics


Analysis on random graphs

Monocentric algorithm behavior analyzed on 3700+ random graphs having diverse characteristics


Plan






• Conclusion






• Build an intuitive and supportive interface• Favor the users engagement

Interactions - challenge


Application design http://discoveryhub.co


Application design http://discoveryhub.co


http://discoveryhub.co

COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Examples of evolutions

COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED. Examples of evolutions


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.


Plan






• Conclusion






• Designing exploratory search evaluation protocols• Evaluating all the Discovery Hub algorithms

Evaluation - challenge


Using

Cases

Compared to sSVM [Mirizzi and al., 2010]


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


Very relevant

Not relevant at all

Monocentric evaluations


Very relevant

Not relevant at all

Hypothesis 1: The SSA algorithm gives results at least as relevant as the sVSM one


Very relevant

Not relevant at all

Known

Not known

Hypothesis 2: The SSA algorithm has less degradation than the sVSM one.


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.


Very relevant

Not relevant at all

Hypothesis 4: The explanatory features increase the users’ overall judgments positivity.


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]


•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


Helpful

Not helpful at all

Hypothesis 7: The explanatory features help the users to understand the results


Using

Information visualization

Serge Gainsbourg

Algorithm variantscompared:

• Basis• Criteria• Randomized - 0.5• Randomized - 1

Advanced querying evaluation


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.


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







Plan






• Conclusion


• Contribution 2: their efficient implementation over distant linked datasets




• Contribution 1: four exploratory search algorithms leveraging the linked data semantics

Monocentric

Polycentric

Criteria variant

Randomized variant

…

…

…

…

Contribution summary



• Contribution 1: four exploratory search algorithms leveraging the linked data semantics

Monocentric

Polycentric

Criteria variant

Randomized variant

…

…

…

…





• Contribution 2: efficient algorithm implementation over distant linked datasets








3 user evaluations



Short-term improvements

-Facet and collection-levels interactions

-Collaborative exploratory search

-Post-query re-ranking

-New query-means


Long-term perspectives

- Negative search

- Adaptive queries recommendation

- Collection-based knowledge creation

- On-the-fly distributed data selection

- Massive use and commercial success


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


Other achievements



Other achievements



Other achievements



• 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 ?


Annexes



Tomaso Di Noia and al. Linked Open Data to support

Content-based Recommender Systems. I-Semantics

2012


[Passant, 2010]

[Mirizzi, 2010]

Wateilonis, 2010




p.223


p.216



















Technology

Phd defense - Linked data based exploratory search - Nicolas MARIE