Recommending appropriate collaborators to researchers can promote their research. In many cases, however, it is difficult for researchers to find proper collaborators from large number candidates. This paper proposes a scientific collaborator recommendation approach based on the semantic link networks, where nodes are authors, papers and interests indicated by keywords, and semantic links are write links, cite links, and contain links between these semantic nodes. Five semantic paths on the semantic link networks are proposed for deriving future collaboration between authors. Experiments on three datasets of scientific journal papers show that our method achieves good performance in predicting future collaborators. Comparing the combinations of five semantic paths reaches the following results: (1) co-author relationship, keyword information, and citation relationship play an important role in finding appropriate collaborators; and, (2) combining all the five semantic paths can get the best results on collaborator recommendation task.
|Name||Proceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019|
|Conference||2019 15th International Conference on Semantics, Knowledge and Grids (SKG)|
|Period||17/09/19 → 18/09/19|
- Collaborator recommendation
- Semantic link network
- Semantic paths