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

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

URL: http://arxiv.org/abs/2112.09340v1
Date: Fri, 17 Dec 2021 06:19:37 GMT
Title: KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling
Authors: Yun-Cheng Wang, Xiou Ge, Bin Wang, C.-C. Jay Kuo
Abstract summary: KGBoost is a new method 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. 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.
Score: 29.14178162494542
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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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