Incremental schema integration for data wrangling via knowledge graphs

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Semantic Web Pub Date : 2023-06-08 DOI:10.3233/sw-233347

Javier Flores, Kashif Rabbani, S. Nadal, Cristina Gómez, Oscar Romero, E. Jamin, S. Dasiopoulou

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引用次数: 1

Abstract

Virtual data integration is the current approach to go for data wrangling in data-driven decision-making. In this paper, we focus on automating schema integration, which extracts a homogenised representation of the data source schemata and integrates them into a global schema to enable virtual data integration. Schema integration requires a set of well-known constructs: the data source schemata and wrappers, a global integrated schema and the mappings between them. Based on them, virtual data integration systems enable fast and on-demand data exploration via query rewriting. Unfortunately, the generation of such constructs is currently performed in a largely manual manner, hindering its feasibility in real scenarios. This becomes aggravated when dealing with heterogeneous and evolving data sources. To overcome these issues, we propose a fully-fledged semi-automatic and incremental approach grounded on knowledge graphs to generate the required schema integration constructs in four main steps: bootstrapping, schema matching, schema integration, and generation of system-specific constructs. We also present Nextia DI , a tool implementing our approach. Finally, a comprehensive evaluation is presented to scrutinize our approach.

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通过知识图实现数据争用的增量模式集成

虚拟数据集成是当前数据驱动决策中处理数据争论的方法。在本文中，我们专注于自动化模式集成，它提取数据源模式的同质化表示，并将它们集成到一个全局模式中，以实现虚拟数据集成。模式集成需要一组众所周知的结构:数据源模式和包装器、全局集成模式以及它们之间的映射。在此基础上，虚拟数据集成系统通过查询重写实现快速和按需的数据探索。不幸的是，这种构造的生成目前主要以手工方式执行，阻碍了其在实际场景中的可行性。在处理异构和不断发展的数据源时，这种情况变得更加严重。为了克服这些问题，我们提出了一种基于知识图的完全成熟的半自动增量方法，通过四个主要步骤生成所需的模式集成构造:引导、模式匹配、模式集成和生成系统特定构造。我们还介绍了Nextia DI，这是一个实现我们方法的工具。最后，提出了一个全面的评估，以审查我们的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Semantic Web COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS

CiteScore

8.30

自引率

6.70%

发文量

期刊介绍： The journal Semantic Web – Interoperability, Usability, Applicability brings together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future internet and elsewhere. As such, Semantic Web technologies shall support the seamless integration of data, on-the-fly composition and interoperation of Web services, as well as more intuitive search engines. The semantics – or meaning – of information, however, cannot be defined without a context, which makes personalization, trust, and provenance core topics for Semantic Web research. New retrieval paradigms, user interfaces, and visualization techniques have to unleash the power of the Semantic Web and at the same time hide its complexity from the user. Based on this vision, the journal welcomes contributions ranging from theoretical and foundational research over methods and tools to descriptions of concrete ontologies and applications in all areas. We especially welcome papers which add a social, spatial, and temporal dimension to Semantic Web research, as well as application-oriented papers making use of formal semantics.