Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion (Student Abstract)

Ziyue Qiao, Zhiyuan Ning, Yi Du, Yuanchun Zhou

AAAI 2021 pp. 15871-15872

doi:10.1609/AAAI.V35I18.17932 /aaai/2021/qiao2021aaai-context/

Abstract

Most researches for knowledge graph completion learn representations of entities and relations to predict missing links in incomplete knowledge graphs. However, these methods fail to take full advantage of both the contextual information of entity and relation. Here, we extract contexts of entities and relations from the triplets which they compose. We propose a model named AggrE, which conducts efficient aggregations respectively on entity context and relation context in multi-hops, and learns context-enhanced entity and relation embeddings for knowledge graph completion. The experiment results show that AggrE is competitive to existing models.

PDF AAAI Semantic Scholar

Cite

Text

Qiao et al. "Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17932

Markdown

[Qiao et al. "Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/qiao2021aaai-context/) doi:10.1609/AAAI.V35I18.17932

BibTeX

@inproceedings{qiao2021aaai-context,
  title     = {{Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion (Student Abstract)}},
  author    = {Qiao, Ziyue and Ning, Zhiyuan and Du, Yi and Zhou, Yuanchun},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15871-15872},
  doi       = {10.1609/AAAI.V35I18.17932},
  url       = {https://mlanthology.org/aaai/2021/qiao2021aaai-context/}
}