Link Prediction between Group Entities in Knowledge Graphs (Student Abstract)


  • Jialin Su Chinese Academy of Sciences
  • Yuanzhuo Wang Chinese Academy of Sciences
  • Xiaolong Jin Chinese Academy of Sciences
  • Yantao Jia Huawei Technologies Co., Ltd
  • Xueqi Cheng Chinese Academy of Sciences



Link prediction in knowledge graphs (KGs) aims at predicting potential links between entities in KGs. Existing knowledge graph embedding (KGE) based methods represent individual entities and links in KGs as vectors in low-dimension space. However, these methods focus mainly on the link prediction of individual entities, yet neglect that between group entities, which exist widely in real-world KGs. In this paper, we propose a KGE based method, called GTransA, for link prediction between group entities in a heterogeneous network by integrating individual entity links into group entity links during prediction. Experiments show that GTransA decreases mean rank by 5.4%, compared to TransA.




How to Cite

Su, J., Wang, Y., Jin, X., Jia, Y., & Cheng, X. (2020). Link Prediction between Group Entities in Knowledge Graphs (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13925-13926.



Student Abstract Track