Knowledge Graph Completion by Intermediate Variables Regularization

Changyi Xiao, Xiangnan He, Yixin Cao

Primary Area: learning on graphs and other geometries & topologies

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Keywords: Knowledge Graph Completion, Tensor Decomposition, Regularization

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TL;DR: We propose a regularization to alleviate the overfitting problem of tensor decomposition based models for knowledge graph completion.

Abstract: 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, incorporates existing regularization methods, 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.

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Submission Number: 9265