Constructing a knowledge graph for open government data: the case of Nova Scotia disease datasets.

IF 1.6 3区 工程技术 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Enayat Rajabi, Rishi Midha, Jairo Francisco de Souza
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引用次数: 1

Abstract

The majority of available datasets in open government data are statistical. They are widely published by various governments to be used by the public and data consumers. However, most open government data portals do not provide the five-star Linked Data standard datasets. The published datasets are isolated from one another while conceptually connected. This paper constructs a knowledge graph for the disease-related datasets of a Canadian government data portal, Nova Scotia Open Data. We leveraged the Semantic Web technologies to transform the disease-related datasets into Resource Description Framework (RDF) and enriched them with semantic rules. An RDF data model using the RDF Cube vocabulary was designed in this work to develop a graph that adheres to best practices and standards, allowing for expansion, modification and flexible re-use. The study also discusses the lessons learned during the cross-dimensional knowledge graph construction and integration of open statistical datasets from multiple sources.

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构建开放政府数据的知识图谱:以新斯科舍省疾病数据集为例。
开放政府数据中的大多数可用数据集都是统计数据。它们由各国政府广泛发布,供公众和数据消费者使用。然而,大多数开放的政府数据门户网站不提供五星级的关联数据标准数据集。发布的数据集彼此隔离,但在概念上是连接的。本文构建了加拿大政府数据门户网站Nova Scotia Open data的疾病相关数据集的知识图谱。我们利用语义Web技术将疾病相关数据集转换为资源描述框架(RDF),并用语义规则对其进行丰富。本文设计了一个使用RDF Cube词汇表的RDF数据模型,用于开发符合最佳实践和标准的图,允许扩展、修改和灵活重用。研究还讨论了跨维知识图谱构建和多源开放统计数据集集成的经验教训。
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来源期刊
Journal of Biomedical Semantics
Journal of Biomedical Semantics MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
4.20
自引率
5.30%
发文量
28
审稿时长
30 weeks
期刊介绍: Journal of Biomedical Semantics addresses issues of semantic enrichment and semantic processing in the biomedical domain. The scope of the journal covers two main areas: Infrastructure for biomedical semantics: focusing on semantic resources and repositories, meta-data management and resource description, knowledge representation and semantic frameworks, the Biomedical Semantic Web, and semantic interoperability. Semantic mining, annotation, and analysis: focusing on approaches and applications of semantic resources; and tools for investigation, reasoning, prediction, and discoveries in biomedicine.
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