A Re-evaluation of Knowledge Graph Completion Methods
Zhiqing Sun, Shikhar Vashishth, Soumya Sanyal, Partha Talukdar, Yiming Yang
Abstract
Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got published at top conferences in several research fields, including data mining, machine learning, and natural language processing. However, we notice that several recent papers report very high performance, which largely outperforms previous state-of-the-art methods. In this paper, we find that this can be attributed to the inappropriate evaluation protocol used by them and propose a simple evaluation protocol to address this problem. The proposed protocol is robust to handle bias in the model, which can substantially affect the final results. We conduct extensive experiments and report performance of several existing methods using our protocol. The reproducible code has been made publicly available.- Anthology ID:
- 2020.acl-main.489
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5516–5522
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.489
- DOI:
- 10.18653/v1/2020.acl-main.489
- Cite (ACL):
- Zhiqing Sun, Shikhar Vashishth, Soumya Sanyal, Partha Talukdar, and Yiming Yang. 2020. A Re-evaluation of Knowledge Graph Completion Methods. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5516–5522, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Re-evaluation of Knowledge Graph Completion Methods (Sun et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2020.acl-main.489.pdf
- Code
- svjan5/kg-reeval + additional community code
- Data
- FB15k, FB15k-237