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arXiv · 2112.09340

KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling

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

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Yun-Cheng Wang, Xiou Ge, Bin Wang, C. -C. Jay Kuo. 2021-12-17. KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling. https://doi.org/10.1016/j.patrec.2022.04.001

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