STransE: a novel embedding model of entities and relationships in knowledge bases
STransE: a novel embedding model of entities and relationships in knowledge bases
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DOI：
10.18653/v1/n16-1054
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发表时间：
2016-06
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影响因子：
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通讯作者：
Dat Quoc Nguyen;Kairit Sirts;Lizhen Qu;Mark Johnson
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文献类型：
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作者：
Dat Quoc Nguyen;Kairit Sirts;Lizhen Qu;Mark Johnson
关于实体及其关系的真实世界事实的知识库是各种自然语言处理任务的有用资源。然而，因为知识库通常是不完整的，所以能够执行链接预测或知识库补全是有用的，即预测不在知识库中的关系是否可能为真。本文综合了以往几种链接预测模型的思想，提出了一种新的嵌入模型STransE，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，但它在两个基准数据集上获得了比以前的嵌入模型更好的链接预测性能。因此，STransE可以作为链接预测任务中更复杂模型的新基线。
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 or knowledge base completion, 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.