ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources
ROBOKOP KG and KGB: Integrated Knowledge Graphs from Federated Sources
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DOI：
10.1021/acs.jcim.9b00683
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发表时间：
2019-12-01
影响因子：
5.6
通讯作者：
Tropsha, Alexander
中科院分区：
文献类型：
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作者：
Bizon, Chris;Cox, Steven;Tropsha, Alexander
数据源的激增导致了隐含知识图（KG）的概念性存在，该知识图包含由分布式应用程序编程接口（API）贡献的大量生物知识。然而，由于语义类型、标识符方案和数据格式不兼容，在跨多个API集成数据时会出现挑战。我们介绍了ROBOKOP KG（robokopkg.renci.org），这是一个KG，最初是为了支持开放的生物医学问答应用程序，ROBOKOP（推理生物医学对象链接在面向知识的途径）（robokop.renci.org）。此外，我们提出了ROBOKOP知识图生成器（KGB），它构造KG，并提供了一个可扩展的框架来处理图形查询和联邦数据源的集成。
A proliferation of data sources has led to the notional existence of an implicit Knowledge Graph (KG) that contains vast amounts of biological knowledge contributed by distributed Application Programming Interfaces (APIs). However, challenges arise when integrating data across multiple APIs due to incompatible semantic types, identifier schemes, and data formats. We present ROBOKOP KG (http://robokopkg.renci.org), which is a KG that was initially built to support the open biomedical question-answering application, ROBOKOP (Reasoning Over Biomedical Objects linked in Knowledge-Oriented Pathways) (http://robokop.renci.org). Additionally, we present the ROBOKOP Knowledge Graph Builder (KGB), which constructs the KG and provides an extensible framework to handle graph query over and integration of federated data sources.