OntologyGen: A smart software for automatic ontology generation from MongoDB using Formal Concept Analysis

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2025-09-18 DOI:10.1016/j.softx.2025.102333

Elmehdi Elguerraoui , Omar Boutkhoum , Mohamed Hanine , Waeal J. Obidallah

{"title":"OntologyGen: A smart software for automatic ontology generation from MongoDB using Formal Concept Analysis","authors":"Elmehdi Elguerraoui , Omar Boutkhoum , Mohamed Hanine , Waeal J. Obidallah","doi":"10.1016/j.softx.2025.102333","DOIUrl":null,"url":null,"abstract":"<div><div>OntologyGen is a web-based framework that automates OWL ontology generation from MongoDB databases, using Formal Concept Analysis (FCA). Built with Python and Django, It extracts a formal context from NoSQL data, builds concept lattices, and applies rule-based mappings to produce OWL ontologies. OntologyGen offers an interactive graphical interface that requires less user involvement, allows the user to extract semantic structures from schema-flexible data, and then builds OWL ontologies that can be used with other existing tools. By using two publicly available MongoDB datasets of varying complexity, the framework’s usability and efficacy were established, with a subsequent assessment of performance metrics including execution time, memory footprint, and ontology size. It was concluded that OntologyGen represents a considerable opportunity to reduce the difficulty of ontology engineering for data scientists and domain experts, while also providing scalability, interoperability, and extensibility beyond the current implementation with other NoSQL systems or possible future ontology learning extensions.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"32 ","pages":"Article 102333"},"PeriodicalIF":2.4000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoftwareX","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352711025002997","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}

引用次数: 0

Abstract

OntologyGen is a web-based framework that automates OWL ontology generation from MongoDB databases, using Formal Concept Analysis (FCA). Built with Python and Django, It extracts a formal context from NoSQL data, builds concept lattices, and applies rule-based mappings to produce OWL ontologies. OntologyGen offers an interactive graphical interface that requires less user involvement, allows the user to extract semantic structures from schema-flexible data, and then builds OWL ontologies that can be used with other existing tools. By using two publicly available MongoDB datasets of varying complexity, the framework’s usability and efficacy were established, with a subsequent assessment of performance metrics including execution time, memory footprint, and ontology size. It was concluded that OntologyGen represents a considerable opportunity to reduce the difficulty of ontology engineering for data scientists and domain experts, while also providing scalability, interoperability, and extensibility beyond the current implementation with other NoSQL systems or possible future ontology learning extensions.

查看原文本刊更多论文

OntologyGen：一个使用形式概念分析从MongoDB自动生成本体的智能软件

OntologyGen是一个基于web的框架，使用形式概念分析（Formal Concept Analysis， FCA）从MongoDB数据库自动生成OWL本体。它使用Python和Django构建，从NoSQL数据中提取正式上下文，构建概念格，并应用基于规则的映射来生成OWL本体。OntologyGen提供了一个交互式图形界面，需要较少的用户参与，允许用户从模式灵活的数据中提取语义结构，然后构建可以与其他现有工具一起使用的OWL本体。通过使用两个公开可用的不同复杂性的MongoDB数据集，建立了框架的可用性和有效性，随后对性能指标进行了评估，包括执行时间、内存占用和本体大小。结论是，OntologyGen代表了一个相当大的机会，为数据科学家和领域专家降低本体工程的难度，同时也提供了可扩展性、互操作性和可扩展性，超越了当前与其他NoSQL系统或可能的未来本体学习扩展的实现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.