A Re-evaluation of Knowledge Graph Completion Methods

• DOI: 10.18653/v1/2020.acl-main.489
• Publication Date: 2020-07-29T14:14:43Z
• Authors: Zhiqing Sun; Shikhar Vashishth; Soumya Sanyal; Partha Talukdar; Yiming Yang
• Keywords: Knowledge graph

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 top conferences in several research fields, including data mining, machine learning, and natural language processing. However, we notice that recent papers report very high performance, which largely outperforms previous methods. In this paper, find can be attributed to the inappropriate evaluation protocol used by them propose a simple address problem. The proposed is robust handle bias model, substantially affect final results. We conduct extensive experiments performance existing methods using our protocol. reproducible code has been made publicly available.