Calvanese Diego, Lanti Davide, Mendes De Farias Tarcisio, Mosca Alessandro, Xiao Guohui
Faculty of Computer Science, Free University of Bozen-Bolzano, 39100 Bolzano, Italy.
Department of Computing Science, Umeå University, 901 87 Umeå, Sweden.
Patterns (N Y). 2021 Oct 8;2(10):100346. doi: 10.1016/j.patter.2021.100346.
In this tutorial, we learn how to set up and exploit the virtual knowledge graph (VKG) approach to access data stored in relational legacy systems and to enrich such data with domain knowledge coming from different heterogeneous (biomedical) resources. The VKG approach is based on an ontology that describes a domain of interest in terms of a vocabulary familiar to the user and exposes a high-level conceptual view of the data. Users can access the data by exploiting the conceptual view, and in this way they do not need to be aware of low-level storage details. They can easily integrate ontologies coming from different sources and can obtain richer answers thanks to the interaction between data and domain knowledge.
在本教程中,我们将学习如何设置和运用虚拟知识图谱(VKG)方法来访问存储在关系型遗留系统中的数据,并利用来自不同异构(生物医学)资源的领域知识丰富这些数据。VKG方法基于一种本体,该本体根据用户熟悉的词汇描述感兴趣的领域,并呈现数据的高级概念视图。用户可以通过利用概念视图来访问数据,这样他们无需了解底层存储细节。他们可以轻松整合来自不同来源的本体,并且由于数据与领域知识之间的交互,能够获得更丰富的答案。