Article


Article Code : 13981001204200

Article Title : Context-Based Expert Finding in Online Communities Using Ant Colony Algorithm

Journal Number : 30 Spring 2020

Visited : 58

Files : 726 KB


List of Authors

  Full Name Email Grade Degree Corresponding Author
1 Mojtaba Sharifian sharifian68@gmail.com Graduate M.Sc
2 Neda Abdolvand Abdolvand@gmail.com Associate Professor PhD
3 Saeedeh Rajaee Harandi rajaeeharandi.saeedeh@gmail.com Professor PhD

Abstract

Online communities are the most popular interactive environments on the Internet, which provide users with a platform to share their knowledge and expertise. The most important use of online communities in cyberspace is sharing knowledge. These communities are a great place to ask questions and find answers. The important challenges of these communities are the large volume of information and the lack of a method to determine their validity as well as expert finding which attracted a lot of attention in both industry and academia in. Therefore, identifying persons with relevant knowledge on a given topic and ranking them according to their expertise score can help to calculate the accuracy of the comments submitted on the internet. In this research, a model for finding experts and determining their domain expertise level by the aid of statistical calculations and the ant colony algorithm in the MetaFilter online community was presented. The WordNet Dictionary was used to determine the relevance of the user’s questions with the intended domain. The proposed algorithm determines the level of people’s expertise in the intended field by using the pheromone section of the Ant colony algorithm, which is based on the similarity of the questions sent by the users and the shared knowledge of the users from their interactions in the online community