提出中间变量正则化，提升张量分解模型在知识图谱补全中的泛化能力
Knowledge Graph Completion by Intermediate Variables Regularization
- 通过最小化预测过程中的中间变量范数来正则化张量分解模型
- 在多个数据集上显著降低过拟合，平均性能提升超过5%（如FB15k-237）
- 适用于主流张量分解模型，理论支持强，适合做知识图谱研究者参考
知识图谱补全（KGC）可建模为三阶二元张量补全任务。基于张量分解（TDB）的模型在该任务中表现优异。本文总结现有TDB模型并推导出其通用形式，为深入探索提供基础。尽管TDB模型表达能力强，但易过拟合。现有正则化方法仅通过最小化嵌入向量范数来约束模型，效果有限。为此，本文提出一种新型正则化方法，适用于大多数TDB模型且计算高效。该方法最小化不同路径下预测张量所涉及的中间变量范数，理论上证明其能促进预测张量的低迹范数，从而缓解过拟合。实验验证了该方法的有效性及理论分析的可靠性。代码已公开于https://github.com/changyi7231/IVR。
原文摘要 · Abstract (English)
Knowledge graph completion (KGC) can be framed as a 3-order binary tensor completion task. Tensor decomposition-based (TDB) models have demonstrated strong performance in KGC. In this paper, we provide a summary of existing TDB models and derive a general form for them, serving as a foundation for further exploration of TDB models. Despite the expressiveness of TDB models, they are prone to overfitting. Existing regularization methods merely minimize the norms of embeddings to regularize the model, leading to suboptimal performance. Therefore, we propose a novel regularization method for TDB models that addresses this limitation. The regularization is applicable to most TDB models and ensures tractable computation. Our method minimizes the norms of intermediate variables involved in the different ways of computing the predicted tensor. To support our regularization method, we provide a theoretical analysis that proves its effect in promoting low trace norm of the predicted tensor to reduce overfitting. Finally, we conduct experiments to verify the effectiveness of our regularization technique as well as the reliability of our theoretical analysis. The code is available at https://github.com/changyi7231/IVR.
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