The general topic of the thesis is web search. It focused on how to leverage the data semantics for exploratory search. Exploratory search refers to cognitive consuming search tasks that are open-ended, multi-faceted, and iterative like learning or topic investigation. Semantic data and linked data in particular offer new possibilities to solve complex search queries and information needs including exploratory search ones. In this context the linked open data cloud plays an important role by allowing advanced data processing and innovative interactions model elaboration. First, we detail a state-of-the-art review of linked data based exploratory search approaches and systems. Then we propose a linked data based exploratory search solution which is mainly based on an associative retrieval algorithm. We started from a spreading activation algorithm and proposed new diffusion formula optimized for typed graph. Starting from this formalization we proposed additional formalizations of several advanced querying modes in order to solve complex exploratory search needs. We also propose an innovative software architecture based on two paradigmatic design choices. First the results have to be computed at query-time. Second the data are consumed remotely from distant SPARQL endpoints. This allows us to reach a high level of flexibility in terms of querying and data selection. We specified, designed and evaluated the Discovery Hub web application that retrieves the results and present them in an interface optimized for exploration. We evaluate our approach thanks to several human evaluations and we open the discussion about new ways to evaluate exploratory search engines. Main contributions of the thesis: • A state-of-the-art review of semantic exploration systems • Several exploratory search algorithms • Their innovative implementations and calibrations on top of DBpedia (the semantic Wikipedia) • The Discovery Hub exploratory search system web application • The evaluations of the algorithms' results and of the interface: http://discoveryhub.co • A novel experimentation protocol for evaluating exploratory search systems

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Linked data based exploratory search
Phd defense – Nicolas MARIE – 12.12.2014

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10 blue links paradigm,
Simple, fast
Exploratory search
bottleneck
Exploratory search: open search tasks having a general objective
like learning or investigating a poorly known topic
Search is only a partially solved
problem
[White, 2009]

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10 blue links paradigm,
Simple, fast
Exploratory search
bottleneck
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 search
bottleneck

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<1990 Web
<2001 + Semantic
web
<2007 + Linked data
<2012 +Proprietary
KG
Semantic search refers to the incorporation of structured
semantics in search

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Human
Computer
Information
Retrieval
Linked data based exploratory search
Research questions

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Human
Computer
Information
Retrieval
Linked data based exploratory search
Research 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?

8. 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

9. 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 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

10. 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 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

11. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
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
Semantic
weighting

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a
w
g
q
j
k
s m
i
p
e
o
b
n
1
class propagation domain:
Artist, Museum

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a
w
g
q
j
k
s m
i
p
e
o
b
n
1
class propagation domain:
Artist, Museum

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a
w
g
q
s m
p
e
o
n
1
, dcterms:category, ?x
, dcterms:category, ?x
Similarity computation

32. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
a
w
g
q
s 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 Paris
Category:French painters
Category: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 these
resources?
The BeatlesKen Loach
Polycentric algorithm
?????

34. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Composites interest queries: knowing my interest for X and
Y what can I discover/learn which is related to all these
resources?
Ken Loach
Polycentric algorithm
Margaret Thatcher
?????

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Polycentric semantic spreading activation
Propagation domain ( ) Propagation domain ( , )
Bands, Movies, Music genre
Movies, Office Holder,
Political party

41. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Multi-perspectives exploration
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

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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

44. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
, dcterms:category, ?x
, dcterms:category, ?x
Classic similarity measure
, dcterms:category, ?a | ?b | ?c |...
, dcterms:category, ?a | ?b | ?c |...
Criteria spec. similarity
Multi-perspectives

45. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Chosen level of randomness
Multi-perspectives

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Chosen level of randomness
Multi-perspectives

47. 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 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-time
from distant linked datasets

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55. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.

56. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
• Dbpedia 3.7
• 3.4 million resources
• 270 million triples
• Spread in both direction
• Categories based similarity
Settings
Mirizzi and al., 2010

57. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Import size
Result quality Iterations
Trade-off

58. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Algorithm studied on 100.000 representative
DBpedia 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 source
Computation 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

72. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
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

73. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
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

74. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
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

76. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Analysis on random graphs
Monocentric algorithm behavior analyzed on 3700+
random graphs having diverse characteristics

77. 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 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

78. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
• 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

81. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
http://discoveryhub.co

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Examples of evolutions

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Examples of evolutions

84. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
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.

85. 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 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

86. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
• 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]

88. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
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

90. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Very relevant
Not relevant at all
Hypothesis 1: The SSA algorithm gives results at least as releva
the sVSM one

91. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
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.

93. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
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]

95. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
•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

96. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Helpful
Not helpful at all
Hypothesis 7: The explanatory features help the users to understand
results

97. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Using
Information
visualization
Serge
Gainsbourg
Algorithm variants
compared:
• Basis
• Criteria
• Randomized - 0.5
• Randomized - 1
Advanced querying evaluation

98. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
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.

99. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
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 agreeat all 1-2-3-4 Totally agree

100. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.

101. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.

102. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.

103. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.

104. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.

105. 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

106. 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
• How can we discover and rank linked resources to be
explored starting from the user topic(s) of interest?

107. 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

108. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
• How to address remote linked data source for this
selection?
Contribution summary
• Contribution 2: efficient algorithm implementation
over distant linked datasets

109. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
• 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

110. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
• How to evaluate such exploratory search
systems?
• Contribution 4: multiple users’ evaluations of the
Discovery Hub results
3 user evaluations
Contribution summary

111. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Short-term improvements
-Facet and collection-levels interactions
-Collaborative exploratory search
-Post-query re-ranking
-New query-means

112. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Long-term perspectives
- Negative search
- Adaptive queries recommendation
- Collection-based knowledge creation
- On-the-fly distributed data selection
- Massive use and commercial success

113. 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 2012
Winner of the Challenge Jeunes Pousses 2013, in teamSeveral interventions during technological events

114. 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 2012
Winner of the Challenge Jeunes Pousses 2013, in teamSeveral interventions during technological events

115. 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 2012
Winner of the Challenge Jeunes Pousses 2013, in teamSeveral interventions during technological events

116. 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 2012
Winner of the Challenge Jeunes Pousses 2013, in teamSeveral interventions during technological events

117. COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
• Nicolas Marie, Fabien Gandon. Demonstration of multi-perspective exploratory search
with 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-fly
linked 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

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[Passant, 2010]
[Mirizzi, 2010]
Wateilonis, 2010

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