Finding Experts in Community Question Answering System Using Trie String Matching Algorithm with Domain Knowledge

Abstract

Different community question-answering systems have been evolving as online fora for knowledge sharing among users across the world. However, finding experts on the questions is a key issue in the community question-answering system. To address this issue, a novel approach is proposed in this paper for finding experts from the community question-answering website using an exact string-matching algorithm along with domain knowledge. The proposed system is designed with three phases: i) User Profile Modeling, ii) Question Preprocessing, and iii) Expert Recommendation. Initially, the matrix factorization model is adopted for user profile modeling where the tags and the answerer’s information are represented in the form of a matrix. Then, the domain-wise grouping of tags is done to minimize the search time of the tags. The question preprocessing is done using a keyword extraction algorithm to extract the keywords. Finally, the expert recommendation is accomplished using the trie string matching algorithm and key-value mapping process. For doing experiments, a stack overflow community question-answering website is utilized in this work. The performance of the system is measured and the results section proved that the system achieved 91.2% accuracy.

KEYWORDS:

• Community question answering
• Domain-wise experts
• Exact string matching
• Finding experts
• Key-value mapping
• Trie data structure

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

R. Menaha

R Menaha received the BE degree in computer science engineering from Bharadhidhasan University, Trichy, India, in 2003. She completed the ME computer science engineering degree from Anna University, Chennai, India in 2012. She received PhD degree from Anna University, Chennai, India, in 2021. She has 17 years of teaching experience and published 13 research articles in international journals. Her current research areas of interest include machine learning, sentimental analysis, question answering system, heuristics approach, semantic similarity, word sense disambiguation and web mining. Corresponding author. Email: [email protected]

V. E. Jayanthi

V E Jayanthi received the AMIE degree in electronics and communication engineering from the Institution of Engineers (India) in 1994. She completed the ME degree in applied electronics from Anna University, Chennai in 2004. She received PhD degree from Anna University, Chennai in 2013. She is a member of ISTE, IAENG, IEEE (Signal Processing), and the Biomedical Engineering Society of India (BMSI). She has 20 years of teaching and programming experience and published papers in international journals. Her research interests include digital image processing, signal processing & VLSI architecture design and data mining. Email: [email protected]