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首页> 外文会议>Conference on the North American Chapter of the Association for Computational Linguistics: Human Language Technologies >STransE: a novel embedding model of entities and relationships in knowledge bases
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STransE: a novel embedding model of entities and relationships in knowledge bases

机译：STransE：知识库中实体和关系的新型嵌入模型

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Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a low-dimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.
机译：有关实体及其关系的真实事实的知识库是用于各种自然语言处理任务的有用资源。但是，由于知识库通常是不完整的，因此能够执行链接预测，即预测不在知识库中的关系是否可能为真是有用的。本文将来自几个以前的链接预测模型的见解组合到一个新的嵌入模型STransE中，该模型将每个实体表示为一个低维向量，每个关系由两个矩阵和一个平移向量表示。 STransE是SE和TransE模型的简单组合，但是与以前的嵌入模型相比，它在两个基准数据集上具有更好的链接预测性能。因此，STransE可以用作链接预测任务中更复杂模型的新基线。

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