Answer selection and expert finding in community question answering services: A question answering promoter. Issue 1 (3rd April 2017)

• Record Type: Journal Article
• Authors: Wang, Hei-Chia; Yang, Che-Tsung; Yen, Yi-Hao
• Is Part Of: Program. Volume 51:Issue 1(2017)
• Page Start: 17
• Page End: 34
• Publication Date: 2017-04-03
• Subjects: Knowledge sharing -- Community question answering -- Expert finding -- Answer selection -- Bottom-up multipath evaluation -- Question routing; Libraries, University and college -- Great Britain -- Automation -- Periodicals; 025.30285
• DOI: 10.1108/PROG-01-2015-0008
• Languages: English
• ISSNs: 0033-0337

Abstract

Abstract : Purpose: Community question answering (CQA) websites provide an open and free way to share knowledge about general topics on the internet. However, inquirers may not obtain useful answers and those who are qualified to provide answers may also miss opportunities to share their expertise without any notice. To address this problem, the purpose of this paper is to provide the means for inquirers to access archived answers and to identify effective subject matter experts for target questions. Design/methodology/approach: This paper presents a question answering promoter, called QAP, for the CQA services. The proposed QAP facilitates the use of filtered archived answers regarded as explicit knowledge and recommended experts regarded as sources of implicit knowledge for the given target questions. Findings: The experimental results indicate that QAP can leverage knowledge sharing by refining archived answers upon creditability and distributing raised questions to qualified potential experts. Research limitations/implications: This proposed method is designed for the traditional Chinese corpus. Originality/value: This paper proposed an integrated framework of answer selection and expert finding uses the bottom-up multipath evaluation algorithm, an underlying voting model, the agglomerative hierarchical clustering technique and feature approaches of answer trustworthiness measuring, identification of satisfied learners and credibility of repliers. The experiments using the corpus crawled from Yahoo! Knowledge Plus under designed scenarios are conducted and results are shown in fine details.