Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA.
BMC Med Inform Decis Mak. 2022 Jun 2;22(Suppl 2):147. doi: 10.1186/s12911-022-01848-z.
Knowledges graphs (KGs) serve as a convenient framework for structuring knowledge. A number of computational methods have been developed to generate KGs from biomedical literature and use them for downstream tasks such as link prediction and question answering. However, there is a lack of computational tools or web frameworks to support the exploration and visualization of the KG themselves, which would facilitate interactive knowledge discovery and formulation of novel biological hypotheses.
We developed a web framework for Knowledge Graph Exploration and Visualization (KGEV), to construct and visualize KGs in five stages: triple extraction, triple filtration, metadata preparation, knowledge integration, and graph database preparation. The application has convenient user interface tools, such as node and edge search and filtering, data source filtering, neighborhood retrieval, and shortest path calculation, that work by querying a backend graph database. Unlike other KGs, our framework allows fast retrieval of relevant texts supporting the relationships in the KG, thus allowing human reviewers to judge the reliability of the knowledge extracted.
We demonstrated a case study of using the KGEV framework to perform research on COVID-19. The COVID-19 pandemic resulted in an explosion of relevant literature, making it challenging to make full use of the vast and heterogenous sources of information. We generated a COVID-19 KG with heterogenous information, including literature information from the CORD-19 dataset, as well as other existing knowledge from eight data sources. We showed the utility of KGEV in three intuitive case studies to explore and query knowledge on COVID-19. A demo of this web application can be accessed at http://covid19nlp.wglab.org . Finally, we also demonstrated a turn-key adaption of the KGEV framework to study clinical phenotypic presentation of human diseases by Human Phenotype Ontology (HPO), illustrating the versatility of the framework.
In an era of literature explosion, the KGEV framework can be applied to many emerging diseases to support structured navigation of the vast amount of newly published biomedical literature and other existing biological knowledge in various databases. It can be also used as a general-purpose tool to explore and query gene-phenotype-disease-drug relationships interactively.
知识图谱（KGs）是一种方便的知识结构框架。已经开发了许多计算方法来从生物医学文献中生成 KGs，并将其用于下游任务，例如链接预测和问答。然而，缺乏用于探索和可视化 KG 本身的计算工具或网络框架，这将促进交互式知识发现和新的生物学假设的制定。
我们开发了一个用于知识图谱探索和可视化（KGEV）的网络框架，用于在五个阶段构建和可视化 KGs：三元组提取、三元组过滤、元数据准备、知识集成和图形数据库准备。该应用程序具有方便的用户界面工具，例如节点和边缘搜索和过滤、数据源过滤、邻居检索和最短路径计算，这些工具通过查询后端图形数据库来实现。与其他 KGs 不同，我们的框架允许快速检索支持 KG 中关系的相关文本，从而允许人工审查员判断提取知识的可靠性。
我们展示了使用 KGEV 框架进行 COVID-19 研究的案例研究。COVID-19 大流行导致相关文献大量涌现，使得充分利用广泛而异构的信息源变得具有挑战性。我们使用异构信息生成了一个 COVID-19 KG，包括来自 CORD-19 数据集的文献信息，以及来自八个数据源的其他现有知识。我们通过三个直观的案例研究展示了 KGEV 在探索和查询 COVID-19 知识方面的实用性。该网络应用程序的演示可以在 http://covid19nlp.wglab.org 访问。最后，我们还展示了 KGEV 框架在研究人类疾病临床表型呈现方面的即插即用适应，说明了该框架的多功能性。
在文献爆炸的时代，KGEV 框架可应用于许多新兴疾病，以支持对大量新发表的生物医学文献和各种数据库中其他现有生物知识的结构化导航。它还可以用作交互式探索和查询基因-表型-疾病-药物关系的通用工具。
