A graph model with integrated pattern and query-based technique for extracting answer to questions in community question answering system

Abstract

This paper presents an extension of the community question-answering (CQA) system we developed previously. This graph-based system method that builds ranked answers for related questions using nKullback–Leibler (KL) divergence. The process of extracting answers to questions in this work 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 existing data from ResearchGate, a socio-academic networking website that provides researchers the platform to collaborate, ask questions, and offer answers to questions. 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.