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首页> 外文会议>Workshop on Scholarly Document Processing >ERLKG: Entity Representation Learning and Knowledge Graph based association analysis of COVID-19 through mining of unstructured biomedical corpora
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ERLKG: Entity Representation Learning and Knowledge Graph based association analysis of COVID-19 through mining of unstructured biomedical corpora

机译：ERLKG：通过非结构化生物医学基层采矿的Covid-19基于实体表示学习与知识图表的关联分析

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摘要

We introduce a generic, human-out-of-the-loop pipeline, ERLKG, to perform rapid association analysis of any biomedical entity with other existing entities from a corpora of the same domain. Our pipeline consists of a Knowledge Graph (KG) created from the Open Source CORD-19 dataset by fully automating the procedure of information extraction using SciBERT. The best latent entity representations are then found by benchnmark-ing different KG embedding techniques on the task of link prediction using a Graph Convolution Network Auto Encoder (GCN-AE). We demonstrate the utility of ERLKG with respect to COVID-19 through multiple qualitative evaluations. Due to the lack of a gold standard, we propose a relatively large intrinsic evaluation dataset for COVID-19 and use it for validating the top two performing KG embedding techniques. We find TransD to be the best performing KG embedding technique with Pearson and Spearman correlation scores of 0.4348 and 0.4570 respectively. We demonstrate that a considerable number of ERLKG's top protein, chemical and disease predictions are currently in consideration for COVID-19 related research.
机译：我们介绍了一种通用，人类超越的流水线ERLKG，以与来自同一域的Corpora的其他现有实体对任何生物医学实体进行快速关联分析。我们的流水线包括通过使用Scibert的信息提取程序完全自动化从开源线-19数据集创建的知识图（kg）。然后，通过使用图形卷积网络自动编码器（GCN-AE）的链路预测任务的基准嵌入技术找到最佳潜在实体表示。我们通过多种定性评估展示了Erlkg关于Covid-19的效用。由于缺乏黄金标准，我们为Covid-19提出了一个相对较大的内在评估数据集，并使用它来验证前两个执行kg嵌入技术。我们发现Transd是最好的kg嵌入技术，Pearson和Spearman相关评分分别为0.4348和0.4570。我们表明，目前考虑到Covid-19相关研究，目前考虑到相当数量的Erlkg的Top蛋白，化学和疾病预测。

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