$\tau\text{JOWL}$: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Mining and Analytics Pub Date : 2022-07-18 DOI:10.26599/BDMA.2021.9020019

Zouhaier Brahmia;Fabio Grandi;Rafik Bouaziz

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引用次数: 0

Abstract

Nowadays, ontologies, which are defined under the OWL 2 Web Ontology Language (OWL 2), are being used in several fields like artificial intelligence, knowledge engineering, and Semantic Web environments to access data, answer queries, or infer new knowledge. In particular, ontologies can be used to model the semantics of big data as an enabling factor for the deployment of intelligent analytics. Big data are being widely stored and exchanged in JavaScript Object Notation (JSON) format, in particular by Web applications. However, JSON data collections lack explicit semantics as they are in general schema-less, which does not allow to efficiently leverage the benefits of big data. Furthermore, several applications require bookkeeping of the entire history of big data changes, for which no support is provided by mainstream Big Data management systems, including Not only SQL (NoSQL) database systems. In this paper, we propose an approach, named $\tau \text{JOWL}$ (temporal OWL 2 from temporal JSON), which allows users (i) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (ii) to manage its incremental maintenance accommodating the evolution of these data, in a temporal and multi-schema environment.

查看原文本刊更多论文

$\tau\text｛JOWL｝$：一种基于时态JSON大数据构建和演化时态OWL2本体的系统方法

如今，在OWL2 Web本体语言（OWL2）下定义的本体正被用于人工智能、知识工程和语义Web环境等多个领域，以访问数据、回答查询或推断新知识。特别是，本体可以用于对大数据的语义进行建模，作为部署智能分析的一个有利因素。大数据以JavaScript Object Notation（JSON）格式被广泛存储和交换，尤其是通过Web应用程序。然而，JSON数据集合缺乏明确的语义，因为它们通常没有模式，这不允许有效地利用大数据的好处。此外，一些应用程序需要对大数据变化的整个历史进行记账，而主流大数据管理系统（包括Not only SQL（NoSQL）数据库系统）对此没有提供支持。在本文中，我们提出了一种名为$\tau\text｛JOWL｝$（时态JSON中的时态OWL2）的方法，该方法允许用户（i）根据封闭世界假设（CWA），从基于时态JSON的大数据中自动构建时态OWL2数据本体，以及（ii）在时态和多模式环境中管理其增量维护，以适应这些数据的演变。

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

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.