Talk for the 3rd DBpedia community meeting.

1. Julien Plu, Giuseppe Rizzo, Raphaël Troncy
{firstname.lastname}@eurecom.fr,
@julienplu, @giusepperizzo, @rtroncy
Using DBpedia for Spotting and
Disambiguating Entities

2. Agenda
 Entity Linking task
 Why using DBpedia?
 Workflow
 How is the index created?
 Experiments on tweets
 Future work
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3. Entity Linking Task
 The purpose is to link entity mentions one can
find in text to their corresponding entries in a
knowledge base.
 Example:
Last year I went to Paris to see the Eiffel Tower with
some friends.
http://dbpedia.org/resource/Paris http://dbpedia.org/resource/Eiffel_Tower
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4. Why using DBpedia?
 No legacy problems compared with Freebase
 Knowledge base is constantly evolving
 Available in many languages which are
interlinked
 Most of the resources have a type
 All the resources have semantic relations with
others
 Possibility to get the popularity of a resource
for each language
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5. Workflow
Text
POS Tagging /
N-grams
analysis to get
the entities
Lookup in the
index to get
candidates for
each entities
linking each
entity in
choosing the
right one
among the
candidates
• Not domain-dependent
• The lookup and the linking processes are made on top of
an index created with DBpedia
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6. How is the index created?
 3 datasets are used:
Titles
Redirects
Disambiguation links
 Structure of the index:
First column is the label of the entity
Second column is the URI of the entity
Third column list all the labels of the redirect pages
linked to the entity
Fourth column is the label of the disambiguation page of
the entity
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7. Experiments on tweets
 Dataset from the #Micropost2014 NEEL
challenge
 Entity recognition
 Entity recognition + linking
Precision Recall F-measure
31,29% 20,64% 24,88%
Precision Recall F-measure
63,51% 41,91% 50,50%
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8. Future work
 Using deeply DBpedia:
Relation among the entities
Compute the popularity of an entity (i.e pageRank
according to a language)
Relation between different languages for the same entity
Using the types for each entity
 Using better algorithm to rank candidates after
the lookup
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