Question difficulty evaluation by knowledge gap analysis in Question Answer communities

Chih Lu Lin, Ying Liang Chen, Hung Yu Kao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
EditorsMartin Ester, Guandong Xu, Xindong Wu, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages336-339
Number of pages4
ISBN (Electronic)9781479958771
DOIs
Publication statusPublished - 2014 Oct 10
Event2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 - Beijing, China
Duration: 2014 Aug 172014 Aug 20

Publication series

NameASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Other

Other2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
CountryChina
CityBeijing
Period14-08-1714-08-20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Lin, C. L., Chen, Y. L., & Kao, H. Y. (2014). Question difficulty evaluation by knowledge gap analysis in Question Answer communities. In M. Ester, G. Xu, X. Wu, & X. Wu (Eds.), ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 336-339). [6921606] (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2014.6921606
Lin, Chih Lu ; Chen, Ying Liang ; Kao, Hung Yu. / Question difficulty evaluation by knowledge gap analysis in Question Answer communities. ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. editor / Martin Ester ; Guandong Xu ; Xindong Wu ; Xindong Wu. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 336-339 (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining).
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title = "Question difficulty evaluation by knowledge gap analysis in Question Answer communities",
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.",
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Lin, CL, Chen, YL & Kao, HY 2014, Question difficulty evaluation by knowledge gap analysis in Question Answer communities. in M Ester, G Xu, X Wu & X Wu (eds), ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining., 6921606, ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Institute of Electrical and Electronics Engineers Inc., pp. 336-339, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014, Beijing, China, 14-08-17. https://doi.org/10.1109/ASONAM.2014.6921606

Question difficulty evaluation by knowledge gap analysis in Question Answer communities. / Lin, Chih Lu; Chen, Ying Liang; Kao, Hung Yu.

ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ed. / Martin Ester; Guandong Xu; Xindong Wu; Xindong Wu. Institute of Electrical and Electronics Engineers Inc., 2014. p. 336-339 6921606 (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - 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.

AB - 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.

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Lin CL, Chen YL, Kao HY. Question difficulty evaluation by knowledge gap analysis in Question Answer communities. In Ester M, Xu G, Wu X, Wu X, editors, ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Institute of Electrical and Electronics Engineers Inc. 2014. p. 336-339. 6921606. (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining). https://doi.org/10.1109/ASONAM.2014.6921606