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

Jialin Su, Yuanzhuo Wang, Xiaolong Jin, Yantao Jia, Xueqi Cheng

AAAI 2020 pp. 13925-13926

doi:10.1609/AAAI.V34I10.7235 /aaai/2020/su2020aaai-link/

Abstract

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.

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Cite

Text

Su et al. "Link Prediction Between Group Entities in Knowledge Graphs (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7235

Markdown

[Su et al. "Link Prediction Between Group Entities in Knowledge Graphs (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/su2020aaai-link/) doi:10.1609/AAAI.V34I10.7235

BibTeX

@inproceedings{su2020aaai-link,
  title     = {{Link Prediction Between Group Entities in Knowledge Graphs (Student Abstract)}},
  author    = {Su, Jialin and Wang, Yuanzhuo and Jin, Xiaolong and Jia, Yantao and Cheng, Xueqi},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13925-13926},
  doi       = {10.1609/AAAI.V34I10.7235},
  url       = {https://mlanthology.org/aaai/2020/su2020aaai-link/}
}