Anytime Bottom-up Rule Learning for Knowledge Graph Completion

Christian Meilicke, Melisachew Wudage Chekol, Daniel Ruffinelli, Heiner Stuckenschmidt

IJCAI 2019 pp. 3137-3143

doi:10.24963/IJCAI.2019/435 /ijcai/2019/meilicke2019ijcai-anytime/

Abstract

We propose an anytime bottom-up technique for learning logical rules from large knowledge graphs. We apply the learned rules to predict candidates in the context of knowledge graph completion. Our approach outperforms other rule-based approaches and it is competitive with current state of the art, which is based on latent representations. Besides, our approach is significantly faster, requires less computational resources, and yields an explanation in terms of the rules that propose a candidate.

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Cite

Text

Meilicke et al. "Anytime Bottom-up Rule Learning for Knowledge Graph Completion." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/435

Markdown

[Meilicke et al. "Anytime Bottom-up Rule Learning for Knowledge Graph Completion." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/meilicke2019ijcai-anytime/) doi:10.24963/IJCAI.2019/435

BibTeX

@inproceedings{meilicke2019ijcai-anytime,
  title     = {{Anytime Bottom-up Rule Learning for Knowledge Graph Completion}},
  author    = {Meilicke, Christian and Chekol, Melisachew Wudage and Ruffinelli, Daniel and Stuckenschmidt, Heiner},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {3137-3143},
  doi       = {10.24963/IJCAI.2019/435},
  url       = {https://mlanthology.org/ijcai/2019/meilicke2019ijcai-anytime/}
}