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Julien Plu, Giuseppe Rizzo, Raphaël Troncy
{firstname.lastname}@eurecom.fr,
@julienplu, @giusepperizzo, @rtroncy
Using DBp...
Agenda
 Entity Linking task
 Why using DBpedia?
 Workflow
 How is the index created?
 Experiments on tweets
 Future ...
Entity Linking Task
 The purpose is to link entity mentions one can
find in text to their corresponding entries in a
know...
Why using DBpedia?
 No legacy problems compared with Freebase
 Knowledge base is constantly evolving
 Available in many...
Workflow
Text
POS Tagging /
N-grams
analysis to get
the entities
Lookup in the
index to get
candidates for
each entities
l...
How is the index created?
 3 datasets are used:
Titles
Redirects
Disambiguation links
 Structure of the index:
First...
Experiments on tweets
 Dataset from the #Micropost2014 NEEL
challenge
 Entity recognition
 Entity recognition + linking...
Future work
 Using deeply DBpedia:
Relation among the entities
Compute the popularity of an entity (i.e pageRank
accord...
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Using DBpedia for Spotting and Disambiguating Entities

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Talk for the 3rd DBpedia community meeting.

Publié dans : Données & analyses

Using DBpedia for Spotting and Disambiguating Entities

  1. 1. Julien Plu, Giuseppe Rizzo, Raphaël Troncy {firstname.lastname}@eurecom.fr, @julienplu, @giusepperizzo, @rtroncy Using DBpedia for Spotting and Disambiguating Entities
  2. 2. Agenda  Entity Linking task  Why using DBpedia?  Workflow  How is the index created?  Experiments on tweets  Future work 09/02/2015 - 3rd DBpedia Community Meeting – Dublin, Ireland - 2
  3. 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 09/02/2015 - 3rd DBpedia Community Meeting – Dublin, Ireland - 3
  4. 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 09/02/2015 - 3rd DBpedia Community Meeting – Dublin, Ireland - 4
  5. 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 09/02/2015 - 3rd DBpedia Community Meeting – Dublin, Ireland - 5
  6. 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 09/02/2015 - 3rd DBpedia Community Meeting – Dublin, Ireland - 6
  7. 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% 09/02/2015 - 3rd DBpedia Community Meeting – Dublin, Ireland - 7
  8. 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 09/02/2015 - 3rd DBpedia Community Meeting – Dublin, Ireland - 8

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