long-tail relation extraction via knowledge graph embeddings and graph convo
时间: 2023-05-03 09:06:19 浏览: 77
本篇论文研究了如何通过知识图谱嵌入和图卷积来实现长尾关系抽取。
长尾关系是指那些在语料库中出现次数很少的关系。这些关系往往被忽略,但它们对于知识图谱来说是非常重要的,在很多实际应用中也非常有用,比如医疗、金融等。
为了解决这个问题,本篇论文提出了一个基于知识图谱嵌入和图卷积的方法。具体来说,该方法将知识图谱表示为一个低维向量空间,并使用图卷积对其进行表示学习。然后,该方法使用一些先验知识来选择相关的实体和关系对,并将其作为输入,从而训练一个模型来预测它们之间的关系。最终,该方法通过反向传播来优化模型的参数,以提高预测准确度。
实验结果表明,所提出的方法在长尾关系抽取任务方面表现比其他基线方法要好。此外,本文章所提出的整个框架也可以广泛应用于其他知识图谱相关的任务,包括实体识别、实体链接等等。
综上所述,该方法在长尾关系抽取方面提供了一个有效的解决方案,同时也为其他知识图谱相关任务提供了有用的思路和参考。
相关问题
long-tail distribution
Long-tail distribution是指数据集或样本中出现少量标签或类别拥有大多数样本,而大量标签或类别只拥有很少样本的情况。在图数据中,long-tail distribution问题主要指的是节点度分布不均衡,即一些节点拥有非常高的度数,而其他节点的度数相对较低。这种不均衡分布会导致模型对于度数较低的节点关注度不高,从而在tail-node set上表现不佳。
这种问题在图数据中与常规欧式数据上的long-tail distribution问题有所不同。常规欧式数据的long-tail distribution问题主要指的是样本标签不平衡,少量的标签拥有大多数的样本,而大量的标签只有很少的样本。针对这种问题,目前的研究主要思路是通过重新采样和成本敏感学习来解决,其中包括一些解决方案。
在图数据中,解决long-tail distribution问题的方法还需要考虑节点度分布的不均衡。有关这个问题的具体解决方法还需要进一步研究和探索。但是长尾分布的存在与二次供应和需求有关,它驱动了Long Tail在线的发展。此外,尽管幂律是等级销售关系的一个良好的近似值,但对于所有的节点度,斜率并不是恒定的。<span class="em">1</span><span class="em">2</span><span class="em">3</span><span class="em">4</span>
what is integrate-series-tail function?
`integrate-series-tail` is a function used in the definition of stream-based power series expansions. It takes a stream representing the coefficients of a power series, and returns a new stream representing the coefficients of the antiderivative of that series.
Here's the definition of `integrate-series-tail`:
```
(define (integrate-series-tail s)
(stream-cons 0 (add-series-tail
(scale-series-tail s 1/2)
(integrate-series-tail (stream-cdr s)))))
```
The function first creates a new stream with a leading coefficient of 0, since the antiderivative of a power series has no constant term. It then uses two other functions, `add-series-tail` and `scale-series-tail`, to combine and manipulate the coefficients of the input stream.
`add-series-tail` takes two streams representing power series, and returns a new stream representing the sum of those series. `scale-series-tail` takes a stream representing a power series, and a scalar value, and returns a new stream representing the series with each coefficient multiplied by that scalar.
In the definition of `integrate-series-tail`, we use `scale-series-tail` to multiply the input stream by `1/2`, since the antiderivative of a power series is obtained by dividing each coefficient of the original series by the corresponding power of the variable. We then use `add-series-tail` to combine this scaled stream with the recursively computed antiderivative of the tail of the input stream.
This recursive computation of the antiderivative of the tail of the input stream is what allows us to generate the coefficients of the power series for the antiderivative. By repeatedly integrating the series, we can generate the coefficients of the power series for any number of antiderivatives of the original function.