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

2026-04-13

AI Summary

This study addresses the lack of systematic evaluation criteria for ontology reuse, which often leads to semantic inconsistency and unreliable performance. To tackle this issue, the authors propose WiseOWL, a novel methodology that integrates four key dimensions—descriptive completeness (documentation coverage), definitional clarity (semantic embedding alignment), structural connectivity, and hierarchical breadth—into a unified quantitative framework for assessing ontologies intended for reuse. Built upon OWL parsing, RDF transformation, and state-of-the-art semantic embedding techniques, WiseOWL features an interactive visualization platform implemented with Streamlit, offering standardized 0–10 scores and actionable feedback. Experimental validation on six widely used ontologies, including the Plant Ontology and Gene Ontology, demonstrates the approach’s effectiveness in significantly enhancing the objectivity of ontology selection and the overall quality of reuse.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Semantic Web
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

The Semantic Web standardizes concept meaning for humans and machines, enabling machine-operable content and consistent interpretation that improves advanced analytics. Reusing ontologies speeds development and enforces consistency, yet selecting the optimal choice is challenging because authors lack systematic selection criteria and often rely on intuition that is difficult to justify, limiting reuse. To solve this, WiseOWL is proposed, a methodology with scoring and guidance to select ontologies for reuse. It scores four metrics: (i) Well-Described, measuring documentation coverage; (ii) Well-Defined, using state-of-the-art embeddings to assess label-definition alignment; (iii) Connection, capturing structural interconnectedness; and (iv) Hierarchical Breadth, reflecting hierarchical balance. WiseOWL outputs normalized 0-10 scores with actionable feedback. Implemented as a Streamlit app, it ingests OWL format, converts to RDF Turtle, and provides interactive visualizations. Evaluation across six ontologies, including the Plant Ontology (PO), Gene Ontology (GO), Semanticscience Integrated Ontology (SIO), Food Ontology (FoodON), Dublin Core (DC), and GoodRelations, demonstrates promising effectiveness.

Problem

• ontology reuse
• ontology selection
• semantic correctness
• ontological descriptiveness
• Semantic Web

Innovation

• ontology evaluation
• semantic embeddings
• ontology reuse
• structured scoring
• interactive visualization