| Autor: |
Wang, Yun-Cheng, Ge, Xiou, Wang, Bin, Kuo, C. -C. Jay |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Pattern Recognition Letters, 2022 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1016/j.patrec.2022.04.001 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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