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|DOI (Digital Object Identifier) Link:||http://doi.org/10.1007/978-3-642-30284-8_41|
|Google Scholar:||Look up in Google Scholar|
Online communities are prime sources of information. The Web is rich with forums and Question Answering (Q&A) communities where people go to seek answers to all kinds of questions. Most systems employ manual answer-rating procedures to encourage people to provide quality answers and to help users locate the best answers in a given thread. However, in the datasets we collected from three online communities, we found that half their threads lacked best answer markings. This stresses the need for methods to assess the quality of available answers to: 1) provide automated ratings to fill in for, or support, manually assigned ones, and; 2) to assist users when browsing such answers by filtering in potential best answers. In this paper, we collected data from three online communities and converted it to RDF based on the SIOC ontology. We then explored an approach for predicting best answers using a combination of content, user, and thread features. We show how the influence of such features on predicting best answers differs across communities. Further we demonstrate how certain features unique to some of our community systems can boost predictability of best answers.
|Item Type:||Conference Item|
|Copyright Holders:||2012 Springer-Verlag|
|Project Funding Details:||
|Extra Information:||The Semantic Web: Research and Applications
9th Extended Semantic Web Conference, ESWC 2012,
Heraklion, Crete, Greece, May 27-31, 2012.
Edited by Elena Simperl, Philipp Cimiano, Axel Polleres, Oscar Corcho, Valentina Presutti
Lecture Notes in Computer Science 7295
|Keywords:||social semantic web; community question answering; content quality; online communities|
|Academic Unit/Department:||Knowledge Media Institute|
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Harith Alani|
|Date Deposited:||02 Jul 2012 09:36|
|Last Modified:||31 Mar 2016 06:47|
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