profile matching in heterogeneous academic social networks using knowledge graphs

With the increasing popularity of academic social networks, many users join more than one network to benefit from their unique features. however, matching the profiles of a user, despite being crucial for data verification and update synchronization, is challenging due to the differences in profile structures across different networks. in this paper, we propose an academic profile-matching approach that utilizes an academic knowledge graph (akg) to overcome the diversity problem in profile structures. our approach includes three components: (1) candidate profile generation, which retrieves related profiles from the target network based on name similarity to the source profile; (2) profile enrichment, which uses akg to discover relations between the attributes of the source and target profiles; and (3) profile matching, which selects one candidate as a matched profile. through experiments on real-world datasets, we demonstrate that the proposed approach is effective in matching academic profiles across different networks, outperforming state-of-the-art baselines.