Purpose

The purpose of this paper is to identify the concepts, component parts and relationships between vocabularies, linked data and knowledge graphs (KGs) from the perspectives of data and knowledge transitions.

Design/methodology/approach

This paper uses conceptual analysis methods. This study focuses on distinguishing concepts and analyzing composition and intercorrelations to explore data and knowledge transitions.

Findings

Vocabularies are the cornerstone for accurately building understanding of the meaning of data. Vocabularies provide for a data-sharing model and play an important role in supporting the semantic expression of linked data and defining the schema layer; they are also used for entity recognition, alignment and linkage for KGs. KGs, which consist of a schema layer and a data layer, are presented as cubes that organically combine vocabularies, linked data and big data.

Originality/value

This paper first describes the composition of vocabularies, linked data and KGs. More importantly, this paper innovatively analyzes and summarizes the interrelatedness of these factors, which comes from frequent interactions between data and knowledge. The three factors empower each other and can ultimately empower the Semantic Web.

中文翻译：

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从数据到知识：词汇表，链接数据和知识图之间的关系

目的

本文的目的是从数据和知识转移的角度识别词汇，链接的数据和知识图（KG）之间的概念，组成部分和关系。

设计/方法/方法

本文使用概念分析方法。这项研究的重点是区分概念，分析组成和相互关系，以探索数据和知识的转移。

发现

词汇表是准确建立对数据含义的理解的基石。词汇表提供了一个数据共享模型，并在支持链接数据的语义表达和定义模式层方面发挥了重要作用。它们还用于KG的实体识别，对齐和链接。KG由模式层和数据层组成，以多维数据集的形式呈现，这些多维数据集有机地组合了词汇表，链接数据和大数据。

创意/价值

本文首先描述了词汇表，链接数据和KG的组成。更重要的是，本文创新性地分析和总结了这些因素之间的相互关系，这是由于数据和知识之间的频繁交互而产生的。这三个因素相互促进，并最终可以使语义网发挥作用。