Finding hard questions by knowledge gap analysis in question answer communities

Ying Liang Chen, Hung-Yu Kao

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

2 Citations (Scopus)

Abstract

The Community Question Answer (CQA) service is a typical forum of Web2.0 in sharing knowledge among people. There are thousands of questions have been posted and solved every day. Because of the above reasons and the variant users in CQA service, the question search and ranking are the most important researches in the CQA portal. In this paper, we address the problem of detecting the question being easy or hard by means of a probability model. In addition, we observed the phenomenon called knowledge gap that is related to the habit of users and use knowledge gap diagram to illustrate how much knowledge gap in different categories. In this task, we propose an approach called knowledge-gap-based difficulty rank (KG-DRank) algorithm that combines the user-user network and the architecture of the CQA service to solve this problem. The experimental results show our approach leads to a better performance than other baseline approaches and increases the F-measure by a factor ranging from 15% to 20%.

Original languageEnglish
Title of host publicationInformation Retrieval Technology - 6th Asia Information Retrieval Societies Conference, AIRS 2010, Proceedings
Pages370-378
Number of pages9
DOIs
Publication statusPublished - 2010 Dec 1
Event6th Asia Information Retrieval Societies Conference, AIRS 2010 - Taipei, Taiwan
Duration: 2010 Dec 12010 Dec 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6458 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th Asia Information Retrieval Societies Conference, AIRS 2010
CountryTaiwan
CityTaipei
Period10-12-0110-12-03

Fingerprint

Web2.0
Knowledge Sharing
Probability Model
Baseline
Ranking
Diagram
Knowledge
Community
Experimental Results
Architecture

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chen, Y. L., & Kao, H-Y. (2010). Finding hard questions by knowledge gap analysis in question answer communities. In Information Retrieval Technology - 6th Asia Information Retrieval Societies Conference, AIRS 2010, Proceedings (pp. 370-378). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6458 LNCS). https://doi.org/10.1007/978-3-642-17187-1_36
Chen, Ying Liang ; Kao, Hung-Yu. / Finding hard questions by knowledge gap analysis in question answer communities. Information Retrieval Technology - 6th Asia Information Retrieval Societies Conference, AIRS 2010, Proceedings. 2010. pp. 370-378 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Chen, YL & Kao, H-Y 2010, Finding hard questions by knowledge gap analysis in question answer communities. in Information Retrieval Technology - 6th Asia Information Retrieval Societies Conference, AIRS 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6458 LNCS, pp. 370-378, 6th Asia Information Retrieval Societies Conference, AIRS 2010, Taipei, Taiwan, 10-12-01. https://doi.org/10.1007/978-3-642-17187-1_36

Finding hard questions by knowledge gap analysis in question answer communities. / Chen, Ying Liang; Kao, Hung-Yu.

Information Retrieval Technology - 6th Asia Information Retrieval Societies Conference, AIRS 2010, Proceedings. 2010. p. 370-378 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6458 LNCS).

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

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Chen YL, Kao H-Y. Finding hard questions by knowledge gap analysis in question answer communities. In Information Retrieval Technology - 6th Asia Information Retrieval Societies Conference, AIRS 2010, Proceedings. 2010. p. 370-378. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-17187-1_36