DBpedia Discovery Hub Search Engine

COPYRIGHT © 2011 ALCATEL-LUCENT. ALL RIGHTS RESERVED.
Discovery hub - a discovery engine on the top of DBpedia
Nicolas Marie, Fabien Gandon, Damien Legrand, Myriam Ribière

2
CONTEXT
RESEARCH QUESTION
RESEARCH
- Proposition
- Implementation
- Operational prototypes
- Users evaluations
PUBLICATION

3
CONTEXT
RESEARCH QUESTION
RESEARCH
- Proposition
- Implementation
- Users evaluations
PUBLICATION

Discovery Hub
Search is only a partially
solved problem
[ White, 2006]

Discovery Hub
Gary Marchioninni, 2006

Discovery Hub
Exploration/discovery todayLookup today
« Claude Monet » + impressionism« Claude Monet » + birthday

Discovery Hub

Discovery Hub
Search is only a partially
solved problem, White 2006
The degree of structure of the web
content is the determining factor
for the type of functionality that
search engines can provide,
Bizer and al., 2012

Discovery Hub
SEMANTIC WEB
• “The Semantic Web is a mesh of information linked up in such a way as to
be easily processable by machines, on a global scale. You can think of it
as being an efficient way of representing data on the World Wide Web, or
as a globally linked database.” Marianna Sigala, Luisa Mich, Jamie Murphy

Discovery Hub
Tim Berners-Lee, WWW1994
[Stankovic, 2012]

Discovery Hub
3 000+ words, 13p. 191 triples
http://en.wikipedia.org/wiki/Claude_Monet
http://dbpedia.org/resource/Claude_Monet

Discovery Hub
•Accessible through:
- Browsers
- Dumps
- SPARQL endpoint
Select * where {
<http://dbpedia.org/resource/Claude_Monet>
<http://dbpedia.org/property/influencedBy>
?x
}

Discovery Hub
DBpedia
Google
Knowledge
Graph
Linked Open
Data cloud3.77 millions things
270 millions facts
500 millions things
3.5 billions facts
31+ billions facts
Close OpenOpen

Discovery Hub
Google knowledge graph, 2012

Discovery Hub
Since 1995 Since 2007
2001 2007

Discovery Hub
• Linked-data based exploratory
search systems
User interest, start point
Interactive result space
Results choice
Ranking
Sorting/categorization
Explanation
dbpedia:
Claude Monet

Discovery Hub State of the art: Seevl, 2010

Discovery Hub State of the art: Yovisto, 2010

Discovery Hub State of the art: LED, 2010

Discovery Hub State of the art: MORE, 2012

Discovery Hub State of the art: Aemoo, 2011

Discovery Hub State of the art: Kaminskas et al., 2011

Discovery Hub State of the art: Google Knowledge Panel, 2012

25
CONTEXT
RESEARCH QUESTION
RESEARCH
- Proposition
- Implementation
- Users evaluations
PUBLICATION

Discovery Hub
•Challenge: enable on-the-fly linked data processing
for exploratory search
•3 major benefits:
- Results freshness
- Composite exploration enablement
- Fine-grained querying capabilities

Discovery Hub Freshness

Discovery Hub
• 3.77 millions resources
• 2 resources possible combinations: 14.212.900.000.000
• 3 resources possible combinations: 53.582.633.000.000.000.000
&
Composite interest exploration: knowing my interest
for X and Y what can I discover/learn which is related
to all these resources?

Discovery Hub Fine-grained querying capabilities
• Artists_from_Paris
• French_painters --
• Impressionist_painters ++
painted ++
influenced by ++

Discovery Hub Fine-grained querying
• 1960s_science_fiction_films
• American_epic_films
• Films_set_on_the_Moon
• Artificial_intelligence_in_fiction
• Space_adventure_films, …
directed by

31
CONTEXT
RESEARCH QUESTION
RESEARCH
- Proposition
- Implementation
- Users evaluations
PUBLICATION

Discovery Hub
Refer to publications for
the complete algorithm

Discovery Hub
Spreading activation basis –
monocentric
Claude_Monet
…
…
…
…
…
…
Iteration 0 Iteration 1 Iteration 2

Discovery Hub
Claude_Monet
Musée d’Orsay
Musée de l’Orangerie
…
……
Vincent Van Gogh
Spreading activation basis –
polycentric

Discovery Hub
Wheeler_School
Art Institute of Chicago Gustave_Courbet
Cadmium_sulfide
Farmington_Mountain
DBO:Museum
DBO:ChemicalSubstance
DBO:Mountain
DBO:Artist
cat:Impressionist_p…
cat:Alumni_of_beaux…
2
0 0 0
3 +2
Propagation domain: artist, book,
film, museum, river, television show,
university, writer,…
cat:Impressionist painters
cat:Alumni_of_Beaux_Arts
DBO:School

Discovery Hub
• How to be fast ?
How to execute it fast
On very a large graph

Discovery Hub Very large graph
Locate the processing

Discovery Hub
1.sparql endpoint = http://xxx/sparql
2.seed(s) = xxx_Beatles
3. compute the propagation domain (w(i,o))
(4. find a path between the seeds)
5. import path nodes & their neighbors
6. for(i=1; i<=maxPulse; i++){
7. pulse
8. if(sampleSize <= maxSampleSize){
9. extend the sample
10. }
11.}

Discovery Hub
select distinct ?x ?y where {
service <sparqlEndpoint>
{
select * where {
?a(<…wikiPageWikiLink>|
^<…wikiPageWikiLink>){0,X} :: $path ?b
filter (?a=<resource1> &&?b=<resource2>)
}
}
graph $path {?x ?p ?y}
filter(?x!=<resource1> && ?x!=<resource2>)
}
Path query using Kgram for
polycentric SA

41
CONTEXT
RESEARCH QUESTION
RESEARCH
- Proposition
- Implementation
- Users evaluations
PUBLICATION

Discovery Hub
• Analysis method
Analysis performed on a set of 100.000 queries

0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 5000 10000 15000 20000
KT
Ms
Triples loading limit
Similarity of top 100 results (Kendall-Tau)
from one loading limit to another
maxSampleSize

Similarity of top 100 results (shared results, KT)
from one iteration to another
maxPulse
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10
Kendall-Tau
Sharedresults
Iterations
KT
shared results

Discovery Hub
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
Milliseconds
Queries response time histogram
Response time histogram

Discovery Hub
Algorithm visualization, available @
http://www.youtube.com/user/wearediscoveryhub

Discovery Hub
Polycentric query propagation
visualization, iteration 0
• In red: Claude Monet
• In blue : Musée d’Orsay
• In purple: Recovery

Discovery Hub
Polycentric query propagation
visualization, iteration 6
• In red: Claude Monet
• In blue : Musée d’Orsay
• In purple: Recovery

Semantic spreading activation
Distance A Distance B Gap A - B Max
distance A
Max
distance B
poly1 - top 10 1.53 1.68 0.34 / /
poly2 - top 10 1.52 1.66 0.33 / /
poly1 - top 100 1.90 2.12 0.49 2.60 2.60
poly2 - top 100 1.88 2.11 0.48 2.58 2.58
Polycentric
Polycentric queries, average distances
of top results from each seed.

• We studied the convergence of the algorithm according to various graph
metrics. For this purpose we generated many graphs thanks to the
Graphstream graph library, conclusion : the diameter is crucial.
Discovery Hub Influence of graph diameter
on algorithm convergence
http://graphstream-project.org/doc/Generators/

Discovery Hub
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8 9 1011121314151617181920
Resultssimilarity
Iterations
diamètre 4.14
diamètre 6.72
diamètre 9.94
diamètre 13.28
diamètre 15.43
diamètre 19.59
diamètre 22.03
diamètre 24.87
diamètre 28.85
Influence of graph diameter

Discovery Hub
0
10
20
30
40
50
60
70
80
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Averagerankvariation
Iterations
diamètre 4.14
diamètre 6.72
diamètre 9.94
diamètre 13.28
diamètre 15.43
diamètre 19.59
diamètre 22.03
diamètre 24.87
diamètre 28.85
Influence of graph diameter

• Discovery hub is an exploratory search engine which helps you to
discover things you might like or be interested in. It widens your
cultural and knowledge horizons by revealing and explaining
unattended information.
• It allows performing queries in an innovative way and helps you to
navigate rich results. As a hub, it proposes redirections to others
platforms to make you benefit from your discoveries (Youtube,
Deezer and more). The results are explained in depth thanks to 3
explanatory features.
• Discovery Hub supports simple and composite explorations i.e.
starting from one or several items of interest. It proposes and is able
to combine advanced exploration modes such as serendipitous,
multi-lingual, and fine-grained ones.
Discovery Hub powered

54
CONTEXT
RESEARCH QUESTION
RESEARCH
- Proposition
- Implementation
- Users evaluations
PUBLICATION

Discovery Hub
1. Start from what you
like or are interested in
2.
Explore, discover, under
stand
3. Be redirected on great
platforms to experience
your discoveries
powered
Book

Discovery Hub V1

Discovery Hub V2

• 3 features to understand the results: common properties
Discovery Hub

Discovery Hub
• 3 features to understand the results: Wikipedia crossed references

Discovery Hub
• 3 features to understand the results: explanatory graph

Discovery Hub Internationalization

Discovery Hub Serendipitous mode
?
?
?
?
Claude_Monet
…
?
…
?
?
…
?
…
?
…
?
…
Iteration 0 Iteration 1 Iteration 2

Discovery Hub Multi-lingual mode
dbpedia:Claude_Monet sameAs fr.dbpedia:Claude_Monet

Discovery Hub Fine search mode
1960s_science_fiction_films
Films_set_on_the_Moon
Artificial_intelligence_in_fiction
Space_adventure_films
Top 4 films

Discovery Hub
Directed by Stanley Kubrick
Top 4 films
Fine search mode

Discovery Hub
1960s_science_fiction_films
Films_set_on_the_Moon
Artificial_intelligence_in_fiction
Space_adventure_films
Directed by Stanley Kubrick
Top 4 films
Fine search mode

Discovery Hub Multi-criterias mode

Discovery Hub
« Surprise mode »
Multi-lingual
Fine-search
Multi-criterias mode

Discovery Hub
Demo videos, available @
http://www.youtube.com/user/wearediscoveryhub

70
CONTEXT
RESEARCH QUESTION
RESEARCH
- Proposition
- Implementation
- Users evaluations
PUBLICATION

Discovery Hub
• Mono-centric queries: evaluated positively on movie domain against
another algorithm: the sSVM implemented in MORE movie recommender
Very
interesting
Not
interesting
at all

Scores for partial lists
Discovery Hub

Discovery Hub - Films_about_criticism_and_refusal_of_work
- Anti-modernist_films
- Fiction_with_unreliable_narrators
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Neutral Personalized
Interesting

Discovery Hub • Poly-centric queries: evaluated positively
0
0.5
1
1.5
2
2.5
3
0
0.5
1
1.5
2
2.5
3
Relevance
Discovery
Very
interesting
Not interesting
at all
Very surprizing
Not suprizing
at all

Discovery Hub
•Recovery between relevance and unexpectedness:
- 61.6% of results were rated as strongly relevant or relevant
by the participants.
- 65% of results were rated as strongly unexpected or
unexpected.
- 35.42% of results were rated both as strongly relevant or
relevant and strongly unexpected or unexpected.

Discovery Hub •Explanation features
0
0.5
1
1.5
2
2.5
3
In
Common
Wikipedia Graph Overall
Monocentric Polycentric
Common
prop.
Wiki-based Graph-based Overall
Very
Helpful
Not helpful
At all

Publications:
• Nicolas Marie, Fabien Gandon, Myriam Ribière, Florentin Rodio, Discovery Hub:
on-the-fly linked data exploratory search. I-Semantics 2013, Graz, 4 – 6
september (paper).
• Nicolas Marie, Fabien Gandon, Damien Legrand, Myriam Ribière, Exploratory
Search on the top of DBpedia chapters with the Discovery Hub Application.
ESWC2013, Montpellier, 26 – 30 may (demo+poster).
• Nicolas Marie, Olivier Corby, Fabien Gandon, Myriam Ribière, Composite interests
exploration thanks to on-the-fly linked data spreading activation, Hypertext 2013,
1-3 may, Paris (paper).
• Clare J. Hooper, Nicolas Marie, Evangelos Kalampokis, Dissecting the Butterfly:
Representation of Disciplines Publishing at the Web Science Conference Series,
Web Science 2012, Northeastern university, Evanston, United States, 22-24 june
(paper).
• Nicolas Marie, Fabien Gandon. Advanced social objects recommendation in
multidimensional social networks. Social Object Workshop 2011, MIT, Boston, USA
(paper).
Discovery Hub

78
CONTEXT
RESEARCH QUESTION
RESEARCH
- Proposition
- Implementation
- Users evaluation
PUBLICATION

Discovery Hub
ncmarie3&@gmail.com
http://ncmarie.tumblr.com
http://discoveryhub.co
werarediscoveryhub@gmail.com
Thank you !

DBpedia Discovery Hub Search Engine

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