An ontological approach for representing declarative mapping languages

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

Semantic Web Pub Date : 2022-12-29 DOI:10.3233/sw-223224

Ana Iglesias-Molina, Andrea Cimmino, E. Ruckhaus, David Chaves-Fraga, R. García-Castro, Óscar Corcho

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引用次数: 3

Abstract

Knowledge Graphs are currently created using an assortment of techniques and tools: ad hoc code in a programming language, database export scripts, OpenRefine transformations, mapping languages, etc. Focusing on the latter, the wide variety of use cases, data peculiarities, and potential uses has had a substantial impact in how mappings have been created, extended, and applied. As a result, a large number of languages and their associated tools have been created. In this paper, we present the Conceptual Mapping ontology, that is designed to represent the features and characteristics of existing declarative mapping languages to construct Knowledge Graphs. This ontology is built upon the requirements extracted from experts experience, a thorough analysis of the features and capabilities of current mapping languages presented as a comparative framework; and the languages’ limitations discussed by the community and denoted as Mapping Challenges. The ontology is evaluated to ensure that it meets these requirements and has no inconsistencies, pitfalls or modelling errors, and is publicly available online along with its documentation and related resources.

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表示声明性映射语言的本体论方法

目前，知识图谱的创建使用了各种各样的技术和工具:编程语言中的特殊代码、数据库导出脚本、OpenRefine转换、映射语言等。关注后者，各种各样的用例、数据特性和潜在用途对如何创建、扩展和应用映射产生了重大影响。因此，大量的语言及其相关工具被创建出来。在本文中，我们提出了概念映射本体，该本体旨在表示现有声明性映射语言的特征和特征，以构建知识图。该本体是建立在专家经验中提取需求的基础上，对当前映射语言的特征和能力进行了全面分析，作为一个比较框架提出的;以及社区讨论的语言局限性，并将其标记为映射挑战。对本体进行评估，以确保它满足这些需求，并且没有不一致、陷阱或建模错误，并且与其文档和相关资源一起在网上公开可用。

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.