Declarative RDF graph generation from heterogeneous (semi-)structured data: A systematic literature review

IF 2.1 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Web Semantics Pub Date : 2023-01-01 DOI:10.1016/j.websem.2022.100753

Dylan Van Assche , Thomas Delva , Gerald Haesendonck , Pieter Heyvaert , Ben De Meester , Anastasia Dimou

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引用次数: 16

Abstract

More and more data in various formats are integrated into knowledge graphs. However, there is no overview of existing approaches for generating knowledge graphs from heterogeneous (semi-)structured data, making it difficult to select the right one for a certain use case. To support better decision making, we study the existing approaches for generating knowledge graphs from heterogeneous (semi-)structured data relying on mapping languages. In this paper, we investigated existing mapping languages for schema and data transformations, and corresponding materialization and virtualization systems that generate knowledge graphs. We gather and unify 52 articles regarding knowledge graph generation from heterogeneous (semi-)structured data. We assess 15 characteristics on mapping languages for schema transformations, 5 characteristics for data transformations, and 14 characteristics for systems. Our survey paper provides an overview of the mapping languages and systems proposed the past two decades. Our work paves the way towards a better adoption of knowledge graph generation, as the right mapping language and system can be selected for each use case.

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从异构（半）结构化数据生成声明性RDF图：系统文献综述

越来越多的各种格式的数据被集成到知识图中。然而，没有对从异构（半）结构化数据生成知识图的现有方法进行概述，这使得很难为特定用例选择合适的方法。为了支持更好的决策，我们研究了基于映射语言从异构（半）结构化数据生成知识图的现有方法。在本文中，我们研究了用于模式和数据转换的现有映射语言，以及生成知识图的相应物化和虚拟化系统。我们收集并统一了52篇关于从异构（半）结构化数据生成知识图的文章。我们评估了用于模式转换的映射语言的15个特征、用于数据转换的5个特征和用于系统的14个特征。我们的调查文件概述了过去二十年中提出的映射语言和系统。我们的工作为更好地采用知识图生成铺平了道路，因为可以为每个用例选择正确的映射语言和系统。

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来源期刊

Journal of Web Semantics 工程技术-计算机：人工智能

CiteScore

6.20

自引率

12.00%

发文量

审稿时长

14.6 weeks

期刊介绍： The Journal of Web Semantics is an interdisciplinary journal based on research and applications of various subject areas that contribute to the development of a knowledge-intensive and intelligent service Web. These areas include: knowledge technologies, ontology, agents, databases and the semantic grid, obviously disciplines like information retrieval, language technology, human-computer interaction and knowledge discovery are of major relevance as well. All aspects of the Semantic Web development are covered. The publication of large-scale experiments and their analysis is also encouraged to clearly illustrate scenarios and methods that introduce semantics into existing Web interfaces, contents and services. The journal emphasizes the publication of papers that combine theories, methods and experiments from different subject areas in order to deliver innovative semantic methods and applications.