RESCAL 论文笔记
A Three-Way Model for Collective Learning on Multi-Relational Data
- - 预备知识
- - Abstract
- - Background
- - Algorithm
- - Experment
- - Conclusion
- 预备知识
克罗内克积(Kronecker product)
https://baike.baidu.com/item/%E5%85%8B%E7%BD%97%E5%86%85%E5%85%8B%E7%A7%AF/6282573?fr=aladdin
- Abstract
we present a novel approach to relational learning based on the factorization of a three-way tensor.
method is able to perform collective learning via the latent components of the model and provide an efficient algorithm to compute the factoriza- tion.
- Background
最开始的张量分解法,无法实现集体学习(捕捉相关实体的属性、关系或类别);随后的DEDICOM可以实现,但是有约束条件,无法合理的关系学习。
well-known tensor factorization approaches such as CANDECOMP/PARAFAC (CP) (Harshman & Lundy, 1994) or Tucker (Tucker, 1966) cannot model this collective learning effect sufficiently. The DEDICOM de- composition (Harshman, 1978) is capable of detecting this type of correlations, but unfortunately, it puts constraints on the model that are not reasonable for relational learning in general and thus leads to suboptimal results.
对比现有的关系学习的方法,会有更好或者相似的实验结果,但是耗时更少。
- Algorithm
语义网中的RDF模型由(subject, predicate, object) 组成的。在张量中,1、0代表实体之间的关系是否存在。
参数说明:
打分函数:
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