QASM: a Q&A Social Media System Based on Social Semantics

Zide Meng; Fabien Gandon; Catherine Faron-Zucker

QASM: a Q&A Social Media System Based on Social Semantics

Zide Meng ^{1, 2} Fabien Gandon ^{1, 2} Catherine Faron-Zucker ^{1, 2}

1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics

CRISAM - Inria Sophia Antipolis - Méditerranée , SPARKS - Scalable and Pervasive softwARe and Knowledge Systems

2 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis

Abstract : In this paper, we describe the QASM (Question & Answer Social Media) system based on social network analysis to manage the two main resources in CQA sites: users and contents. We first present the QASM vocabulary used to formalize both the level of interest and the expertise of users on topics. Then we present our method to extract this knowledge from CQA sites. Finally we show how this knowledge is used both to find relevant experts for a question and to search for similar questions. We tested QASM on a dataset extracted from the popular CQA site StackOverflow.

Keywords : Social Media Mining Se-mantic Web Community Question Answering

Type de document :

Poster

The 13th International Semantic Web Conference, ISWC 2014, Oct 2014, Riva del Garda, Italy

Domaine :

Informatique [cs] / Web

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https://hal.archives-ouvertes.fr/hal-01187455
Contributeur : Catherine Faron Zucker <>
Soumis le : mercredi 26 août 2015 - 17:17:56
Dernière modification le : mardi 19 juillet 2016 - 01:04:06
Document(s) archivé(s) le : vendredi 27 novembre 2015 - 11:04:46

qasm_iswc2014.pdf

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