论文标题
通过语义可视化探索和发现Covid-19文献
Exploration and Discovery of the COVID-19 Literature through Semantic Visualization
论文作者
论文摘要
我们正在开发语义可视化技术，以增强探索并在复杂关系的大型数据集上进行发现。语义可视化是一种通过利用它们中关系的语义来在复杂网络的大型数据集上探索和发现的一种方法。这涉及（i）NLP从原始数据中提取指定的实体，关系和知识图； （ii）为所有相关实体和关系创建输出并创建可以以许多不同方式可视化的表示，例如标签云，热图，图表等； （iii）将参数还原操作应用于提取的关系，创建“关系容器”或也可以使用相同方法可视化的功能实体，从而可以看到多个维度的多个关系，部分路径和探索。我们的希望是，这将使在复杂数据中的关系中发现新颖的推论，否则将不会被忽略。我们将其应用于最近发布的CORD-19数据集的分析。
We are developing semantic visualization techniques in order to enhance exploration and enable discovery over large datasets of complex networks of relations. Semantic visualization is a method of enabling exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them. This involves (i) NLP to extract named entities, relations and knowledge graphs from the original data; (ii) indexing the output and creating representations for all relevant entities and relations that can be visualized in many different ways, e.g., as tag clouds, heat maps, graphs, etc.; (iii) applying parameter reduction operations to the extracted relations, creating "relation containers", or functional entities that can also be visualized using the same methods, allowing the visualization of multiple relations, partial pathways, and exploration across multiple dimensions. Our hope is that this will enable the discovery of novel inferences over relations in complex data that otherwise would go unnoticed. We have applied this to analysis of the recently released CORD-19 dataset.
