WiseOWL: A Methodology for Evaluating Ontological Descriptiveness and Semantic Correctness for Ontology Reuse and Ontology Recommendations
Authors: Aryan Singh Dalal, Maria Baloch, Asiyah Yu Lin, Anna Maria Masci, Kathleen M. Jagodnik, Hande Kucuk McGinty
Organizations: Department of Computer Science, Kansas State University, Manhattan, Kansas, USA · National Center for Ontological Research (NCOR), University at Buffalo, Buffalo, NY, USA · University of Texas MD Anderson Cancer Center, The University of Texas System, Houston, Texas, USA
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.
Figures & tables
Fig. 1: WiseOWL UI application displaying evaluation results for the Plant Ontology (PlantON), highlighting evaluation metrics i.e. Well-Described score, Well-Defined score, Connection score, and Hierarchical Breadth score, along with the overall average score. (Please note each metric is abbreviated as Describe, Define, Connection, and Hierarchy in the Application)
Fig. 2: WiseOWL UI application displaying evaluation results for the Gene Ontology (GO), highlighting evaluation metrics – Well-Described score, Well-Defined score, Connection score, and Hierarchical Breadth score —-along with the overall average score. (Please note each metric is abbreviated as Describe, Define, Connection, and Hierarchy in the Application)
Ontology
Describe
Define
Conn.
Hier.
Avg.
PO
9.97
6.07
7.39
10.00
8.36
GO
10.00
6.29
7.45
10.00
8.43
SIO
10.00
5.72
2.21
10.00
6.98
FoodON
10.00
5.13
2.48
10.00
6.90
DC
9.17
5.71
0.00
6.00
5.22
GoodRelations
9.29
6.09
3.87
8.00
6.81
TABLE I: Ontology scores across metrics (0–10). Higher is better.
Due to the dynamics of the Semantic Web, version control is necessary to manage changes in widely used ontologies. Despite the long-standing recognition of ontology versioning (OV) as a crucial component of efficient ontology management, many approaches treat OV as similar to ontology matching (OM) and directly reuse OM systems for OV tasks. In this study, we systematically analyse similarities and differences between OM and OV and formalise an OM4OV framework to offer more advanced OV support. The framework is implemented and evaluated in the state-of-the-art OM system Agent-OM. The experimental results indicate that OM systems can be effectively reused for OV tasks, but without the necessary extensions, can produce skewed measurements, poor performance in detecting update entities, and limited explanations of false mappings. To tackle these issues, we propose an optimisation method called the cross-reference (CR) mechanism, which builds on existing OM alignments to reduce the number of matching candidates and to improve overall OV performance.
Zhangcheng Qiang, Kerry Taylor, Weiqing Wang
Australian National University, Canberra, Australia · Monash University, Melbourne, Australia
We present Open Ontologies, an open-source ontology engineering system implemented in Rust that integrates LLM-driven construction with formal OWL reasoning and ontology alignment via the Model Context Protocol. Our primary finding is that stable 1-to-1 matching is the dominant factor in ontology alignment quality: on the OAEI Anatomy track, it achieves F1 = 0.832 (P = 0.963, R = 0.733), competitive with state-of-the-art systems and exceeding all in precision. Ablation across five weight configurations shows that signal weights are irrelevant when stable matching is applied (F1 varies by less than 0.004), while removing stable matching drops F1 to 0.728. On the Conference track, the same method achieves F1 = 0.438. On tool-augmented ontology interaction, we find a surprising result: an LLM reading a raw OWL file (F1 = 0.323) performs worse than the same LLM with no file at all (F1 = 0.431), while structured MCP tool access achieves F1 = 0.717. This demonstrates that tool structure provides a qualitatively different mode of access that the LLM cannot replicate by reading raw syntax. The system ships as a single binary under the MIT licence.
Ontology documentation, visualization, and validation are usually carried out with separate tools. This split workflow slows down development and makes knowledge transfer harder. We present OntoInk, an open-source MkDocs plugin that brings these activities together. Within a single documentation-as-code pipeline, OntoInk renders interactive ontology diagrams, validates instance data against SHACL shapes, runs OWL,DL reasoning, and supports inline Turtle editing. General-purpose diagram plugins for MkDocs cannot parse RDF, dereference IRIs, overlay SHACL constraints, or run OWL reasoning. Compared with standalone ontology visualization tools, OntoInk embeds interactive and editable diagrams directly into documentation pages. A live demo and source code are available at https://ise-fizkarlsruhe.github.io/ontoink/.
Ebrahim Norouzi, Jörg Waitelonis, Harald Sack
FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Germany · Karlsruhe Institute of Technology (KIT), Institute AIFB, Germany