Evaluating the Quality of Educational Answers in Community Question-Answering

Purpose Recently, with a high volume of users and user’s content in Community Question Answering (CQA) sites, the quality of answers provided by users has raised a big concern. Finding the expert users can be a method to address this problem, which aims to find the suitable users (answerers) who can provide high-quality relevant answers. The purpose of this paper is to find the expert users for the newly posted questions of the CQA sites. Design/methodology/approach In this paper, a new algorithm, RANKuser, is proposed for identifying the expert users of CQA sites. The proposed RANKuser algorithm consists of three major stages. In the first stage, folksonomy relation between users, tags, and queries is established. User profile attributes, namely, reputation, tags, and badges, are also considered in folksonomy. In the second stage, expertise scores of the user are calculated based on reputation, badges, and tags. Finally, in the third stage, the expert users are identified by extracting top N users based on expertise score. Findings In this work, with the help of proposed ranking algorithm, expert users are identified for newly posted questions. In this paper, comparison of proposed user ranking algorithm (RANKuser) is also performed with other existing ranking algorithms, namely, ML-KNN, rankSVM, LDA, STM CQARank, and EV-based model using performance parameters such as hamming loss, accuracy, average precision, one error, F-measure, and normalized discounted cumulative gain. The proposed ranking method is also compared to the original ranking of CQA sites using the paired t-test. The experimental results demonstrate the effectiveness of the proposed RANKuser algorithm in comparison with the existing ranking algorithms. Originality/value This paper proposes and implements a new algorithm for expert user identification in CQA sites. By utilizing the folksonomy in CQA sites and information of user profile, this algorithm identifies the experts.

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A hybrid MCDM model combining DANP with TODIM to evaluate the information quality of community question answering in a two-dimensional linguistic environment

Expert Systems ◽

10.1111/exsy.12619 ◽

2020 ◽

pp. e12619

Author(s):

Ming Li ◽

Ying Li ◽

Qijin Peng ◽

Jun Wang

Keyword(s):

Information Quality ◽

Question Answering ◽

Two Dimensional ◽

Community Question Answering ◽

Model Combining

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An improvement in the quality of expert finding in community question answering networks

Decision Support Systems ◽

10.1016/j.dss.2020.113425 ◽

2020 ◽

Vol 139 ◽

pp. 113425

Author(s):

Mahdi Dehghan ◽

Ahmad Ali Abin ◽

Mahmood Neshati

Keyword(s):

Question Answering ◽

Expert Finding ◽

Community Question Answering

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Knowledge-enhanced Hierarchical Attention for Community Question Answering with Multi-task and Adaptive Learning

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/743 ◽

2019 ◽

Author(s):

Min Yang ◽

Lei Chen ◽

Xiaojun Chen ◽

Qingyao Wu ◽

Wei Zhou ◽

...

Keyword(s):

Knowledge Base ◽

Adaptive Learning ◽

Question Answering ◽

Factual Knowledge ◽

Essential Information ◽

Adaptive Boosting ◽

Community Question Answering ◽

Task Learning ◽

Semantic Compositionality

In this paper, we propose a Knowledge-enhanced Hierarchical Attention for community question answering with Multi-task learning and Adaptive learning (KHAMA). First, we propose a hierarchical attention network to fully fuse knowledge from input documents and knowledge base (KB) by exploiting the semantic compositionality of the input sequences. The external factual knowledge helps recognize background knowledge (entity mentions and their relationships) and eliminate noise information from long documents that have sophisticated syntactic and semantic structures. In addition, we build multiple CQA models with adaptive boosting and then combine these models to learn a more effective and robust CQA system. Further- more, KHAMA is a multi-task learning model. It regards CQA as the primary task and question categorization as the auxiliary task, aiming at learning a category-aware document encoder and enhance the quality of identifying essential information from long questions. Extensive experiments on two benchmarks demonstrate that KHAMA achieves substantial improvements over the compared methods.

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A Novel Expert Finding System for Community Question Answering

Complexity ◽

10.1155/2020/5346085 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Nan Zhao ◽

Jia Cheng ◽

Nan Chen ◽

Fei Xiong ◽

Peng Cheng

Keyword(s):

Online Communities ◽

Question Answering ◽

Experimental Results ◽

Ranking Algorithm ◽

Expert Finding ◽

Community Question Answering ◽

Expert Ranking

With the popularity of community question answering (CQA) sites, the research on identifying the expert users in online communities attracted increasing attention. We present a novel expert ranking algorithm based on the quality of user posts and the authority of user in community, and the similarity between the knowledge tags of users and questions in CQA sites is adopted in our scheme. Experimental results show that our scheme has better performance and accuracy under the same background with an amount of data samples.

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