论文标题
集群作为评估方案，用于临床领域中分类的多关系数据的知识嵌入表示
Clustering as an Evaluation Protocol for Knowledge Embedding Representation of Categorised Multi-relational Data in the Clinical Domain
论文作者
论文摘要
学习知识表示是一种越来越重要的技术，适用于许多特定领域的机器学习问题。我们讨论传统的链接预测或知识图完成评估协议的有效性，当嵌入临床领域中的多个关系数据的知识表示。链接预测用来将数据拆分为培训和评估子集，从而导致培训中信息丢失并损害知识表示模型的准确性。我们提出了一个聚类评估协议，作为传统使用评估任务的替代方法。我们使用了通过临床数据集评估的知识嵌入方法训练的嵌入模型。 Pearson和Spearman相关性的实验结果表明，新型提出的评估方案可以替代链接预测。
Learning knowledge representation is an increasingly important technology applicable in many domain-specific machine learning problems. We discuss the effectiveness of traditional Link Prediction or Knowledge Graph Completion evaluation protocol when embedding knowledge representation for categorised multi-relational data in the clinical domain. Link prediction uses to split the data into training and evaluation subsets, leading to loss of information along training and harming the knowledge representation model accuracy. We propose a Clustering Evaluation Protocol as a replacement alternative to the traditionally used evaluation tasks. We used embedding models trained by a knowledge embedding approach which has been evaluated with clinical datasets. Experimental results with Pearson and Spearman correlations show strong evidence that the novel proposed evaluation protocol is pottentially able to replace link prediction.
