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.

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SUMMARYThe inertia weight is the control parameter that tunes the balance between the exploration and exploitation movements in particle swarm optimization searches. Since the introduction of inertia weight, various strategies have been proposed for determining the appropriate inertia weight value. This paper presents a brief review of the various types of inertia weight strategies which are classified and discussed in four categories: static, time varying, dynamic, and adaptive. Furthermore, a novel entropy-based gain regulator (EGR) is proposed to detect the evolutionary state of particle swarm optimization in terms of the distances from particles to the current global best. And then apply proper inertia weights with respect to the corresponding distinct states. Experimental results on five widely applied benchmark functions show that the EGR produced significant improvements of particle swarm optimization.