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<p>Ontologies allow us to organize, disseminate, and reuse domain-specific knowledge efficiently. However, creating an ontology entirely from the ground up is often a labor-intensive and error-prone endeavor, requiring considerable time and effort. To address this, our study introduces a refined approach for extracting Web Ontology Language (OWL) ontologies from NoSQL databases, with a focus on MongoDB. This approach is founded on formal concept analysis and includes mapping guidelines to facilitate the automated construction of a detailed and meaningful ontology from complex NoSQL structures. The methodology involves three core stages: first, defining a formal context using data from a MongoDB database; second, employing formal concept analysis to build a concept lattice based on this context; and finally, transforming the concept lattice into a preliminary ontology draft. The approach was validated on five MongoDB databases from diverse domains (music, healthcare, movies, e-commerce, and geography), ranging from 4 to 33 collections and up to 100,000 documents, demonstrating consistent ontology generation with practical execution times. All generated ontologies passed consistency verification using the HermiT reasoner.</p>

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Web ontology language engineering from MongoDB using formal component analysis

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Elguerraoui Elmehdi,
  • Boutkhoum Omar,
  • Hanine Mohamed,
  • Nagwan Abdel Samee,
  • Jin-Ghoo Choi,
  • Imran Ashraf

摘要

Ontologies allow us to organize, disseminate, and reuse domain-specific knowledge efficiently. However, creating an ontology entirely from the ground up is often a labor-intensive and error-prone endeavor, requiring considerable time and effort. To address this, our study introduces a refined approach for extracting Web Ontology Language (OWL) ontologies from NoSQL databases, with a focus on MongoDB. This approach is founded on formal concept analysis and includes mapping guidelines to facilitate the automated construction of a detailed and meaningful ontology from complex NoSQL structures. The methodology involves three core stages: first, defining a formal context using data from a MongoDB database; second, employing formal concept analysis to build a concept lattice based on this context; and finally, transforming the concept lattice into a preliminary ontology draft. The approach was validated on five MongoDB databases from diverse domains (music, healthcare, movies, e-commerce, and geography), ranging from 4 to 33 collections and up to 100,000 documents, demonstrating consistent ontology generation with practical execution times. All generated ontologies passed consistency verification using the HermiT reasoner.