Knowledge Graphs~(KGs) often suffer from unreliable knowledge, which restricts their utility. Triple Classification~(TC) aims to determine the validity of triples from KGs. Recently, text-based methods learn entity and relation representations from natural language descriptions, significantly improving the generalization capabilities of TC models and setting new benchmarks in performance. However, there are still two critical challenges. First, existing methods often ignore the effective semantic interaction among different KG components. Second, most approaches adopt single binary classification training objective, leading to insufficient semantic representation learning. To address these challenges, we propose \textbf{SASA}, a novel framework designed to enhance TC models via separated attention mechanism and semantic-aware contrastive learning~(CL). Specifically, we first propose separated attention mechanism to encode triples into decoupled contextual representations and then fuse them through a more effective interactive way. Then, we introduce semantic-aware hierarchical CL as auxiliary training objective to guide models in improving their discriminative capabilities and achieving sufficient semantic learning, considering both local level and global level CL. Experimental results across two benchmark datasets demonstrate that SASA significantly outperforms state-of-the-art methods. In terms of accuracy, we advance the state-of-the-art by +5.9\% on FB15k-237 and +3.4\% on YAGO3-10.

翻译：知识图谱（KGs）常因存在不可靠知识而限制其实际应用。三元组分类（TC）旨在判定知识图谱中三元组的有效性。近年来，基于文本的方法通过从自然语言描述中学习实体与关系表示，显著提升了TC模型的泛化能力，并在性能上创造了新的基准。然而，当前仍面临两大关键挑战：其一，现有方法往往忽略知识图谱各组成部分间的有效语义交互；其二，多数方法采用单一的二分类训练目标，导致语义表示学习不充分。为应对这些挑战，本文提出\textbf{SASA}——一种通过分离注意力机制与语义感知对比学习（CL）增强TC模型的新型框架。具体而言，我们首先提出分离注意力机制，将三元组编码为解耦的上下文表示，并通过更有效的交互方式进行融合；随后引入语义感知分层对比学习作为辅助训练目标，综合考虑局部层面与全局层面的对比学习，以指导模型提升判别能力并实现充分的语义学习。在两个基准数据集上的实验结果表明，SASA显著优于现有最优方法。在准确率指标上，我们在FB15k-237和YAGO3-10数据集上分别将最优性能提升了+5.9\%和+3.4\%。