CL4KGE: A Curriculum Learning Method for Knowledge Graph Embedding

• URL: http://arxiv.org/abs/2408.14840v2
• Date: Mon, 9 Sep 2024 06:57:22 GMT
• Title: CL4KGE: A Curriculum Learning Method for Knowledge Graph Embedding
• Authors: Yang Liu, Chuan Zhou, Peng Zhang, Yanan Cao, Yongchao Liu, Zhao Li, Hongyang Chen,
• Abstract summary: We define a metric Z-counts to measure the difficulty of training each triple in knowledge graphs. Based on this metric, we propose textbfCL4KGE, an efficient textbfCurriculum textbfLearning based training strategy.
• Score: 36.47838597326351
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge graph embedding (KGE) constitutes a foundational task, directed towards learning representations for entities and relations within knowledge graphs (KGs), with the objective of crafting representations comprehensive enough to approximate the logical and symbolic interconnections among entities. In this paper, we define a metric Z-counts to measure the difficulty of training each triple ($<$head entity, relation, tail entity$>$) in KGs with theoretical analysis. Based on this metric, we propose \textbf{CL4KGE}, an efficient \textbf{C}urriculum \textbf{L}earning based training strategy for \textbf{KGE}. This method includes a difficulty measurer and a training scheduler that aids in the training of KGE models. Our approach possesses the flexibility to act as a plugin within a wide range of KGE models, with the added advantage of adaptability to the majority of KGs in existence. The proposed method has been evaluated on popular KGE models, and the results demonstrate that it enhances the state-of-the-art methods. The use of Z-counts as a metric has enabled the identification of challenging triples in KGs, which helps in devising effective training strategies.

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