TY - GEN
T1 - LLM-Powered Entity Alignment for Enhanced Scientific Collaborator Recommendation
AU - Wang, Jiaxiao
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2025/8
Y1 - 2025/8
N2 - Entity misalignment in knowledge graphs (KGs)—caused by noisy data or inconsistent naming—severely undermines the accuracy of academic collaborator recommendation systems. To address this, we propose a KG-based scholar recommendation system featuring a novel LLM-powered entity alignment method. Our-stage approach first identifies potential matches unsupervisedly, then leverages LLMs' semantic understanding for precise alignment. This high-fidelity alignment directly enhances KG quality, leading to more accurate recommendations. By resolving core entity ambiguity issues, our system aims to significantly improve recommendation reliability. Evaluation on real-world datasets will validate the effectiveness of the alignment method and its impact on recommendation performance. ©ICEB.
AB - Entity misalignment in knowledge graphs (KGs)—caused by noisy data or inconsistent naming—severely undermines the accuracy of academic collaborator recommendation systems. To address this, we propose a KG-based scholar recommendation system featuring a novel LLM-powered entity alignment method. Our-stage approach first identifies potential matches unsupervisedly, then leverages LLMs' semantic understanding for precise alignment. This high-fidelity alignment directly enhances KG quality, leading to more accurate recommendations. By resolving core entity ambiguity issues, our system aims to significantly improve recommendation reliability. Evaluation on real-world datasets will validate the effectiveness of the alignment method and its impact on recommendation performance. ©ICEB.
KW - collaboration recommendation
KW - entity alignment
KW - Knowledge graphs
KW - large language models
KW - recommendation systems
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-105021212254&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings of the International Conference on Electronic Business (ICEB)
SP - 279
EP - 288
BT - Proceedings of The International Conference on Electronic Business, Volume 25
A2 - Li, Eldon Y.
A2 - Loi, Ta Van
A2 - Wang, William Y.C.
T2 - 25th International Conference on Electronic Business (ICEB 2025)
Y2 - 21 August 2025 through 25 August 2025
ER -