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KGBoost: A classification-based knowledge base completion method with negative sampling
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DOI：10.1016/j.patrec.2022.04.001.png)
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Abstract
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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. (c) 2022 Elsevier B.V. All rights reserved.
Keywords:
Knowledge base completion
Negative sampling
Binary classification
XGBoost Classifiers
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