ROBOKOP: an abstraction layer and user interface for knowledge graphs to support question answering
ROBOKOP: an abstraction layer and user interface for knowledge graphs to support question answering
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DOI：
10.1093/bioinformatics/btz604
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发表时间：
2019-12-15
期刊：
影响因子：
5.8
通讯作者：
Tropsha, Alexander
中科院分区：
文献类型：
--
作者：
Morton, Kenneth;Wang, Patrick;Tropsha, Alexander
总结：知识图（KGs）正在迅速成为存储实体之间关系的常用工具，从中可以进行更高级别的推理。KG通常以图形数据库格式存储，图形数据库查询可用于回答用户（如生物医学研究人员）提出的感兴趣的问题。对于简单的查询，在KG中包含直接连接以及查询结果的存储和分析是简单的;然而，对于复杂的查询，随着查询复杂性的增加，这些功能变得更具挑战性。例如，一个相对复杂的查询可以产生具有数十万个查询结果的KG。因此，有效地查询、存储、排名和探索复杂KG的子图的能力代表了对任何旨在利用KG用于生物医学研究和其他领域中的应用的努力的主要挑战。我们提出了推理的生物医学对象链接在面向知识的路径作为一个抽象层和用户界面，更容易查询KG和存储，排名和探索查询结果。
A Summary: Knowledge graphs (KGs) are quickly becoming a common-place tool for storing relationships between entities from which higher-level reasoning can be conducted. KGs are typically stored in a graph-database format, and graph-database queries can be used to answer questions of interest that have been posed by users such as biomedical researchers. For simple queries, the inclusion of direct connections in the KG and the storage and analysis of query results are straightforward; however, for complex queries, these capabilities become exponentially more challenging with each increase in complexity of the query. For instance, one relatively complex query can yield a KG with hundreds of thousands of query results. Thus, the ability to efficiently query, store, rank and explore sub-graphs of a complex KG represents a major challenge to any effort designed to exploit the use of KGs for applications in biomedical research and other domains. We present Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways as an abstraction layer and user interface to more easily query KGs and store, rank and explore query results.