KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling
This work addresses knowledge base completion for AI systems by improving link prediction accuracy and efficiency, though it appears incremental as it builds on existing classification and negative sampling techniques.
The authors tackled knowledge base completion by formulating it as a binary classification problem using XGBoost for each relation, and their method, KGBoost, outperformed state-of-the-art methods on most benchmark datasets while enabling smaller model sizes in low-dimensional settings.
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.