A Graph Model with Integrated Pattern and Query Based Technique for Extracting Answer to Questions in Community Question Answering System

In community question answering (CQA) systems, topical comments are very valuable to provide information for users. However, it becomes cumbersome going through all these in order to decipher the correct answers to particular questions. Hence, extracting a particular answer to a question becomes vital to avoid reading every comment in the forum. This paper is an extension of of our previous research work that extracted questions from an online forum to develop a system for answer extraction to questions. This system is based on a graph-based method by building answers for related questions using nKullback–Leibler (KL) divergence to obtain ranked answers to a question. The process of extracting answers to questions involves; question core, building question query, query-based answer extraction (QBAE), pattern-based answer extraction (PBAE), and combined answer extraction. The source data for this work were already existing data from ResearchGate, a socio-academic networking website that provides researchers the platform collaborate, ask question and offer answers to question. The performance for answer extraction for 2786 questions shows that when 80% of patterns and keywords were considered, QBAE and PBAE extracted 2765 and 2766 correct answers respectively, while the QBAE + PBAE method extracted 2782 correct answers. Also, when 90% of patterns and keywords were utilized, QBAE and PBAE extracted 2782 and 2784 correct answers, whereas the QBAE + PBAE method extracted 2786 correct answers. Our method was able to identify 229 questions without answers. Finally, the evaluation of our model reveals high-performance accuracy and precision.