AEFS : Authoritative Expert Finding System Based on a Language Model and Social Network Analysis

Searching for experts on a given topic is a critical problem in many real-world situations, such as collaborative finding. Even so, previous work has only focused on searching for experts based on the appearance of topic query in an organization’s documents, which means that the experts selected might not be suitable for the task at hand. To resolve this problem, we propose an Authoritative Expert Finding System, called AEFS, which ranks the publications of experts to indicate their level of expertise. AEFS uses non-textual information, e.g. impact factor, to represent the quality of publications, and provides a citation matching function that removes duplicated citations based on the concept of centrality in social network analysis (SNA). In our experiments, we compare a number of related approaches to show that: (1) the proposed approach achieves a good performance in terms of the average F-measure; (2) citation matching can reduce the number of training examples required; and (3) non-textual features are very effective for searching for experts.

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