Expert Profile Identification From Community Detection on Author-Publication-Keyword Graph With Keyword Extraction

Expert profiling aims to discover the expertise of an author. This task is useful for identifying the research groups existing within an organization as well as measuring the similarities between authors’ expertise. Thus, identifying areas of expertise becomes a critical part of this task, especially in cases where the publications are unannotated. Commonly used topic modeling methods such as Latent Dirichlet Allocation still fall short in determining the number of topics automatically and discovering the hierarchical relationships between topics. To solve these issues, we adopted a graph-based approach which constructs a graph from publication features such as authors and keywords (Silva et al., 2018). We applied the Louvain algorithm repeatedly to discover the topics with hierarchical order automatically. We utilize keyword extraction methods to generate keywords for each respective publication to handle the missing values. We perform experiments to determine the optimum HPMI value. Results showed that graphs constructed from default and SIFRank keywords with transformation weights of $\alpha =0.5$ and $\beta =1.0$ produce topics with the best HPMI score. We evaluate the profiles from this method (CDT) with ATM as the baseline. It is shown that CDT produces better MAP, MRR, and nDCG scores than ATM. The work in this manuscript shows how community detection and keyword extraction could be utilized in expert profiling tasks. Our observation shows that the Louvain algorithm used only cluster publications into one topic, and thus still has limitations in classifying multidisciplinary publications. Further development could be done to handle such publications and increase the quality of keywords.