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数据:Freebase containing 1M entities, 25k relationships and more than 17M training samples.
创新点
将三元组embedding
手法
Wordnet synsets 同义词集. We considered the data version used in [2], which wedenote WN in the following. Examples of triplets are (scoreNN1,hypernym,evaluationNN1)or (scoreNN2,haspart,musicalnotationNN1
WN is composed of senses, its entities are denoted by the concatenation of a word, its part-of-speech tagand a digit indicating which sense it refers to i.e.scoreNN1encodes the first meaning of the noun “score”
总结
embedding entities and relationships of multi-relational data in low-dimensional vector spaces.
论文链接/代码
作者/机构
发表时间(yyyy/MM/dd)
概要
Multi-relational data 指的是directed graphs,其中地node与entities和edges相关。
应用场景
本文的工作是从KB(wordnet freebase)中建模,目的是自动添加new fact,即自动添加各种关系。
Modeling multi-relational data
对于single-relaitonal data,用一些描述性分析也能做很多预测,而relational data的难点在于locality(局部)可能会涉及多个关系,多个实体,而且种类会不一样。我们需要一个更普通的方法来考虑各种模式,对multi-relaional data进行建模,来同时捕捉所有的heterogeneous relationships(异质关系)。
Relationships as translations in the embedding space
relationships are represented as translations in the embedding space: (h, l, t)
这个模型的契机有两点。一是hierarchical relationship在了KB中很常见。比如对于一个tree结构的node进行表示,其emebdding应该接近于它的相邻node。第二点是word2vec模型的出现。
数据:Freebase containing 1M entities, 25k relationships and more than 17M training samples.
创新点
将三元组embedding
手法
Wordnet synsets 同义词集. We considered the data version used in [2], which wedenote WN in the following. Examples of triplets are (scoreNN1,hypernym,evaluationNN1)or (scoreNN2,haspart,musicalnotationNN1
WN is composed of senses, its entities are denoted by the concatenation of a word, its part-of-speech tagand a digit indicating which sense it refers to i.e.scoreNN1encodes the first meaning of the noun “score”
结果
relationship根据head和tail分为四种类:1-TO-1, 1-TO-MANY, MANY-TO-1, MANY-TO-MANY.
We obtained that FB15k has 26.2% of 1-TO-1relationships, 22.7% of 1-TO-MANY, 28.3% of MANY-TO-1, and 22.8% of MANY-TO-MANY.
评论
transE参数设置问题 #31
code
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