TY - GEN
T1 - Automatic identification of best answers in online enquiry communities
AU - Burel, Grégoire
AU - He, Yulan
AU - Alani, Harith
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84861746094&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-30284-8_41
DO - 10.1007/978-3-642-30284-8_41
M3 - Conference publication
AN - SCOPUS:84861746094
SN - 978-3-642-30283-1
VL - 7295
T3 - Lecture notes in computer science
SP - 514
EP - 529
BT - The semantic web : research and applications
A2 - Simperl, Elena
A2 - Cimiano, Philipp
A2 - Polleres, Axel
A2 - Corcho, Oscar
A2 - Presutti, Valentina
PB - Springer
CY - Heidelberg (DE)
T2 - 9th extended semantic web conference, ESWC 2012
Y2 - 27 May 2012 through 31 May 2012
ER -