Talk for the 3rd DBpedia community meeting.

1. Julien Plu
julien.plu@eurecom.fr
@julienplu
Populating DBpedia FR and using it for
Extracting Information

2. Agenda
 Mapping the French infoboxes
 How is DBpedia FR used at Orange?
 Presentation of the Orange challenge
 Project: ExtSem
Module 1: ParseText
Module 2: BuildDepGraph
Module 3: ExtractRDF
Module 4: SelectRDF
 Experiments
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3. Mapping the French infoboxes
 The set of mappings has grown significantly
during the last three years (2012-2015)
208 infoboxes have mappings
I contribute to 100 mappings
This amounts to 50% of the articles in the French
Wikipedia which have an infobox
 Example:
Infobox Communes de France (mapping): 36765
occurrences
Infobox Musique (œuvre) (mapping): 29429 occurrences
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4. How is DBpedia FR used at Orange?
 Used as a knowledge graph for the in-house
Web search engine
 Used to interlink background knowledge with
internal data about films (AlloCine) and music
(Deezer)
 Used as a knowledge provider for public tools
in IPTV
 Used for recommendation system in VOD
service
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5. Presentation of the Orange challenge
 Team members:
Guillaume Viland
Jonathan Marchand
Julien Plu
 Internal challenge for getting new research
projects
 Only two weeks to get something to present
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6. Project : ExtSem
 Goal: extracting relations among named
entities in raw text
 Example:
L'excentrique Lady Gaga est au coeur de l'actu depuis
qu'elle a dévoilé son single "Applause" issu de son
quatrième album à découvrir à partir du 11 novembre.
 Results:
Subject predicate object
Lady Gaga etre aucoeurdeactu
Lady Gaga devoiler Applause (chanson)
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7. Module 1: ParseText
.txt
Tokenizer
et PoS
Tagger :
Melt
.conll06
.inmalt
Parser :
MaltParser
• Part of Speech Tagger and
Parser are stochastic and
trained with the French
Dependency Treebank
• Deep syntactic analysis with
dependencies
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8. Module 2: BuildDepGraph
.conll06 .nerd
buildDe
pGraph
.depnt
• This module merges
the output from the
NERD framework with
the syntactic analysis
• The output is in RDF
modeled with a
vocabulary mapped on
French POS tags
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9. Module 3: ExtractRDF
 .depnt example
.depnt
extractRdf .fullnt
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10. Module 4: selectRDF
.fullnt
selectRd
f
.nt
• This module enables to select
the triples who has a URI as
subject
• One can also customize this
module according to a topic
to map the predicate to
properties from well-known
vocabularies
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11. Experiments
 We have processed, for one month, the (480) daily
articles from the “Closer” Magazine.
 Some statistics:
2800 triples extracted
971 distinct entities
657 distinct predicates
At least 4 triples extracted per articles
 Qualitative analysis:
57% of the triples are about relationship between
celebrities (wedding, cheating, rumors, etc.)
43% of the triples are about diverse topics such as sport,
fashion or politics
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12. Conclusion
 Good results for two weeks of work (3rd
position on 7 participants for this challenge)
 The idea behind this project has been taken by
Orange Labs for being exploited
 Possible evolutions:
Automatic mapping of the predicates
Add more grammar rules to get more triples
Improve the performance (slow and long process)
Machine learning algorithm to classify which triple can be
useful (interesting) or not.
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