WiseOWL 2.0 : A Methodology for Evaluating Ontological Descriptiveness and Semantic Correctness for Ontology Reuse and Ontology Recommendations

Ontologies support shared meaning across Semantic Web applications, but selecting an appropriate ontology for reuse remains difficult because existing candidates often differ in documentation quality, semantic clarity, structural connectivity, and logical soundness. Reuse decisions are therefore frequently based on familiarity or intuition rather than reproducible evidence. This paper extends WiseOWL [1], a schema-level methodology and web-based tool for evaluating ontology reuse readiness from an ontology’s own RDF/OWL content. WiseOWL defines four core quality metrics: Well-Described, which measures the coverage of human-readable annotations; Well-Defined, which evaluates the semantic alignment and adequacy of labels and definitions using transformer-based embeddings and textual heuristics; Connection, which assesses the breadth, diversity, and richness of object-property relationships; and Hierarchical Breadth, which measures the balance of class-taxonomy depth and branching, including hierarchy inferred from complex OWL axioms. To complement these aggregate scores, this extension introduces three diagnostic metrics: Logical Consistency, Structural Distinctiveness, and Semantic Distinctiveness, which identify unsatisfiable classes, redundant subclass assertions, and candidate near-duplicate concepts, respectively. The methodology is now formalized using RDF graph notation, bounded score definitions, algorithmic pseudocode, and stated metric properties. WiseOWL is implemented as a Streamlit application that accepts OWL files, converts them to RDF Turtle, computes normalized 0–10 scores, and presents interactive visualizations with exportable results. Evaluation is extended to seven publicly available ontologies—Plant Ontology (PO), Gene Ontology (GO), Semanticscience Integrated Ontology (SIO), Food Ontology (FoodON), Friend of a Friend (FOAF), Dublin Core Terms, and GoodRelations. The results show that WiseOWL produces distinguishable quality profiles and identifies concrete areas for improvement.