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Rule Mining for Semantifying Wikilink

Luis Galárraga 1, 2 Danai Symeonidou 1, 2 Jean-Claude Moissinac 3, 4
1 DIG - Data, Intelligence and Graphs
LTCI - Laboratoire Traitement et Communication de l'Information
3 MM - Multimédia
LTCI - Laboratoire Traitement et Communication de l'Information
Abstract : Wikipedia-centric Knowledge Bases (KBs) such as YAGO and DBpedia store the hyperlinks between articles in Wikipedia using wikilink relations. While wikilinks are signals of semantic connection between entities, the meaning of such connection is most of the times unknown to KBs, e.g., for 89% of wikilinks in DBpedia no other relation between the entities is known. The task of discovering the exact relations that hold between the endpoints of a wikilink is called wikilink semantification. In this paper, we apply rule mining techniques on the already semantified wikilinks to propose relations for the unsemantified wikilinks in a subset of DBpedia. By mining highly supported and confident logical rules from KBs, we can semantify wikilinks with very high precision.
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https://hal.telecom-paris.fr/hal-02412475
Contributor : Telecomparis Hal <>
Submitted on : Sunday, December 15, 2019 - 3:04:56 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:55 PM

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  • HAL Id : hal-02412475, version 1

Citation

Luis Galárraga, Danai Symeonidou, Jean-Claude Moissinac. Rule Mining for Semantifying Wikilink. LODW 2015, May 2015, Florence, Italy. ⟨hal-02412475⟩

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