WiseOWL Methodology Enhances Ontology Reusability

The article presents WiseOWL, a novel methodology designed to enhance the evaluation and reuse of ontologies within the Semantic Web framework. It tackles the challenge of selecting optimal ontologies by introducing scoring criteria across four key metrics: documentation coverage, label-definition alignment, structural interconnectedness, and hierarchical balance. Implemented as a Streamlit application, WiseOWL offers an interactive platform for users to visualize and assess ontologies in OWL format, converting them into RDF Turtle for improved analytics.

The strategic implications of WiseOWL are significant for the field of AI and data management. By providing a systematic approach to ontology selection, it addresses the inherent limitations of intuitive decision-making in ontology reuse, potentially accelerating development timelines and improving consistency across AI applications. This could foster greater interoperability between systems, thereby enhancing overall data quality and efficacy in advanced analytics, aligning with broader goals of data sovereignty and semantic clarity in AI infrastructure.