Pattern-based detection, extraction and analysis of code lists in ontologies and vocabularies

While the early phase of the Semantic Web put emphasis on conceptual modeling through ontology classes, and the recent years saw the rise of loosely structured, instance-level knowledge graphs (used even for modeling concepts), in this paper, we focus on a third kind of concept modeling: via code lists, primarily those embedded in ontologies and vocabularies. We attempt to characterize the candidate structures for code lists based on our observations in OWL ontologies. Our main contribution is then an approach implemented as a series of SPARQL queries and a lightweight web application that can be used to browse and detect potential code lists in ontologies and vocabularies, in order to extract and enhance them, and to store them in a stand-alone knowledge base. The application allows inspecting query results coming from the Linked Open Vocabularies catalog dataset. In addition, we describe a complementary bottom-up analysis of potential code lists. We also provide in this paper a demonstration of the dominant nature of embedded codes from the aspect of ontological universals and their alternatives for modeling code lists.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

This paper focuses on concept modeling through code lists embedded in ontologies and vocabularies. The authors propose an approach using SPARQL queries and a lightweight web application to detect and extract potential code lists, and store them in a knowledge base.