The TransR model solves the problem that TransE and TransH models are not sufficient for modeling in public spaces, and is considered a highly potential knowledge representation model. However, TransR still adopts the translation principles based on the TransE model, and the constraints are too strict, which makes the model’s ability to distinguish between very similar entities low. Therefore, we propose a representation learning model TransR* based on flexible translation and relational matrix projection. Firstly, we separate entities and relationships in different vector spaces; secondly, we combine our flexible translation strategy to make translation strategies more flexible. During model training, the quality of generating negative triples is improved by replacing semantically similar entities, and the prior probability of the relationship is used to distinguish the relationship of similar coding. Finally, we conducted link prediction experiments on the public data sets FB15K and WN18, and conducted triple classification experiments on the WN11, FB13, and FB15K data sets to analyze and verify the effectiveness of the proposed model. The evaluation results show that our method has a better improvement effect than TransR on Mean Rank, [email protected] and ACC indicators.

中文翻译：

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TransR *：通过灵活的翻译和关系矩阵投影的表示学习模型

TransR模型解决了TransE和TransH模型不足以在公共空间中进行建模的问题，并被认为是一种很有潜力的知识表示模型。但是，TransR仍然采用基于TransE模型的转换原理，并且约束条件太严格，这使得该模型区分非常相似的实体的能力较低。因此，我们提出了一种基于柔性平移和关系矩阵投影的表示学习模型TransR *。首先，我们将不同向量空间中的实体和关系分开；其次，我们结合灵活的翻译策略使翻译策略更加灵活。在模型训练期间，通过替换语义相似的实体可以提高生成负三元组的质量，该关系的先验概率用于区分相似编码的关系。最后，我们对公共数据集FB15K和WN18进行了链接预测实验，并对WN11，FB13和FB15K数据集进行了三分类实验，以分析和验证所提出模型的有效性。评估结果表明，我们的方法在平均等级，[受电子邮件保护]和ACC指标上比TransR有更好的改进效果。