Abstract

In recent years, research tasks have increasingly involved using multi-disciplinary knowledge through collaborations of scholars from multiple fields. However, identifying a team of suitable collaborators from diverse fields for a given research task is a challenging and time-consuming process. In this paper, we propose a novel “ScholarTeamFinder” model that uses knowledge graph based link prediction to identify collaborators within an academic social network (ASN) to form a research team to address a multi-disciplinary research problem. Our approach involves building a heterogeneous knowledge graph within an ASN using entities such as scholars, publications, research grants, and the relationship among these entities. Following this, we use graph-based deep learning to learn the node embedding from the knowledge graph that can be used for scholar team recommendation. More specifically, we used the …

Date

October 9, 2023

Authors

Xiyao Cheng, Yuanxun Zhang, Harsh Joshi, Mayank Kejriwal, Prasad Calyam

Conference

2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)

Pages

1-10

Publisher

IEEE