Automatic rule generation using crowdsourcing for better relationship type discovery

Elie Raad 1, * Richard Chbeir 2, * Albert Dipanda 3, * Eliana J. Raad 3, *
* Auteur correspondant
1 Faculty Of Business Administration
2 LIUPPA - Laboratoire Informatique de l'Université de Pau et des Pays de l'Adour
3 Le2i - Laboratoire Electronique, Informatique et Image [UMR6303]
Abstract : With the increasing popularity of information sharing and the growing number of social network users, relationship management is one of the key challenges which arise in the context of social networks. One particular relationship management task aims at identifying relationship types that are relevant between social network users and their contacts. Manually identifying relationship types is one possible solution, however it is a time-consuming and tedious task that requires constant maintenance. In this paper, we present a rule-based approach that sets the focus on published photos as a valuable source to identify relationship types. Our approach automatically generates relevant relationship discovery rules based on a crowdsourcing methodology that constructs useful photo datasets. Knowledge is first retrieved from these datasets and then used to create relationship discovery rules. The obtained set of rules is extended using a number of predefined common sense rules and then personalized using a rule mining algorithm. Experimental results demonstrate the correctness and the efficiency of the generated sets of rules to identify relationship types. (C) 2016 Elsevier B.V. All rights reserved.
Keywords : Relationship discovery Crowdsourcing Knowledge acquisition Rule extraction Link mining Social networks Social network Extraction Privacy Photos Web
Type de document :
Article dans une revue
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01537731
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : lundi 12 juin 2017 - 19:17:12
Dernière modification le : dimanche 7 avril 2019 - 15:00:39

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UNIV-BOURGOGNE | CNRS | UNIV-PAU | LE2I | LIUPPA | ICB | UNIV-HESAM

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Elie Raad, Richard Chbeir, Albert Dipanda, Eliana J. Raad. Automatic rule generation using crowdsourcing for better relationship type discovery. Pervasive and Mobile Computing, Elsevier, 2017, 36, pp.80 - 97. ⟨10.1016/j.pmcj.2016.06.019⟩. ⟨hal-01537731⟩

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