Answer selection and expert finding in community question answering services

• Published in: Program : electronic library and information systems Vol. 51; no. 1; pp. 17 - 34
• Main Authors: Wang, Hei-Chia, Yang, Che-Tsung, Yen, Yi-Hao
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 01.01.2017; Emerald Group Publishing Limited
• Subjects: Accuracy; Algorithms; Answering services; Classification; Cluster analysis; Clustering; Communities; Community; Community Relations; Experiments; Experts; Information Needs; Information processing; Information Seeking; Information sources; Intelligence Quotient; Knowledge sharing; Language Processing; Natural Language Processing; Network Analysis; Problem solving; Quality; Reference services; Researchers; Search engines; Self Evaluation (Individuals); Social Environment; Social Media; Social networks; Social Structure; Studies; Trustworthiness; Usability; User generated content; Websites; Taiwan; Knowledge sharing; Question routing; Bottom-up multipath evaluation; Community question answering; Expert finding; Answer selection
• ISSN: 0033-0337; 2514-9288; 2514-9318
• DOI: 10.1108/PROG-01-2015-0008

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.