KGBOOST: A CLASSIFICATION-BASED KNOWLEDGE BASE COMPLETION METHOD WITH NEGATIVE SAMPLING

Yun-Cheng Wang, Xiou Ge, Bin Wang, C.-C. Jay Kuo

Abstract:

Knowledge base completion is formulated as a binary classification problem in this work, where an
XGBoost binary classifier is trained for each relation using relevant links in knowledge graphs (KGs).
The new method, named KGBoost, adopts a modularized design and attempts to find hard negative
samples so as to train a powerful classifier for missing link prediction. We conduct experiments on
multiple benchmark datasets, and demonstrate that KGBoost outperforms state-of-the-art methods
across most datasets. Furthermore, as compared with models trained by end-to-end optimization,
KGBoost works well under the low-dimensional setting so as to allow a smaller model size.