DocumentCode :
116466
Title :
Question difficulty evaluation by knowledge gap analysis in Question Answer communities
Author :
Chih-Lu Lin ; Ying-Liang Chen ; Hung-Yu Kao
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
fYear :
2014
fDate :
17-20 Aug. 2014
Firstpage :
336
Lastpage :
339
Abstract :
The Community Question Answer (CQA) service is a typical forum of Web 2.0 that shares knowledge among people. There are thousands of questions that are posted and solved every day. Because of the various users of the CQA service, question search and ranking are the most important topics of research in the CQA portal. In this study, we addressed the problem of identifying questions as being hard or easy by means of a probability model. In addition, we observed the phenomenon called knowledge gap that is related to the habit of users and used a knowledge gap diagram to illustrate how much of a knowledge gap exists in different categories. To this end, we proposed an approach called the knowledge-gap-based difficulty rank (KG-DRank) algorithm, which combines the user-user network and the architecture of the CQA service to find hard questions. We used f-measure, AUC, MAP, NDCG, precision@Top5 and concordance analysis to evaluate the experimental results. Our results show that our approach leads to better performance than other baseline approaches across all evaluation metrics.
Keywords :
Internet; diagrams; portals; probability; question answering (information retrieval); AUC; CQA portal; CQA service; KG-DRank algorithm; MAP; NDCG; Web 2.0; community question answer service; concordance analysis; f-measure; knowledge gap analysis; knowledge gap diagram; knowledge-gap-based difficulty rank algorithm; probability model; question difficulty evaluation; question ranking; question search; user-user network; Communities; Conferences; Mathematical model; Measurement; Portals; Social network services; Web sites; CQA portal; Difficulty; Expert finding; Knowledge gap; Link analysis; Social network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on
Conference_Location :
Beijing
Type :
conf
DOI :
10.1109/ASONAM.2014.6921606
Filename :
6921606
Link To Document :
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