Ontology Classification Framework

• The framework for ontologies classification systematically evaluates ontologies based on technical metrics like expressiveness, generality, and logical density.
• It integrates credibility-centered criteria by assessing institutional endorsement, academic recognition, practitioner validation, and industrial maturity.
• Hybrid approaches using ML, clustering, and graph-based methods enhance scalability and automate the alignment of complex ontological structures.

A framework for ontologies classification provides the methodological and operational foundations for systematically categorizing, evaluating, and comparing ontologies according to technical, structural, and, increasingly, credibility-centric criteria. Modern frameworks integrate dimensions ranging from logical expressivity and axiomatization density to external validation and institutional adoption, enabling precise alignment between ontology selection/design and domain requirements.

1. Technical Foundations of Ontologies Classification

A foundational methodology is provided by the Framework for Ontologies Classification (F4OC), which introduces a multi-dimensional, orthogonal pipeline for evaluating candidate ontologies, especially in cybersecurity and allied fields (Leblanc et al., 1 Dec 2025). F4OC is structured as a five-step process:

1. Systematic Mapping: Compilation of a corpus of candidate ontologies and associated metadata (e.g., authorship, publication, format).
2. Level of Application: Distinguishes between Reference Ontologies (RO), designed primarily for consensus and knowledge reuse, and Operational Ontologies (OO), which are concrete, machine-executable artifacts.
3. Level of Generality: Implements a fivefold taxonomy (Foundational, Domain, Core, Task, Application), and checks for Well-Groundedness (WG) by tracing concepts to recognized foundational ontologies (e.g., UFO, DOLCE, BFO).
4. Formal Expressiveness: Quantifies the descriptive power of the representational language used (RDFS, OWL Lite, OWL DL, OWL Full), with higher levels indicating increased access to logical constructors, property chains, and semantic constraints.
5. Logical Density: Measures the ratio of logical axioms to declared vocabulary items; higher values indicate dense, axiomatically rich ontologies, while lower values correspond to lightweight, taxonomic models.

Table: Core Dimensions of F4OC

Dimension	Notation	Possible Values
Reference Ontology	RA	{0, 1}
Operational Ontology	OP	{0, 1}
Well-groundedness	WG	{0, 1}
Level of Generality	—	{Foundational, Domain, Core, Task, Application}
Formal Expressiveness	FE	{1, 2, 3, 4} (RDFS–OWL Full)
Logical Density	LD	ℝ⁺

This multidimensional representation captures technical quality and semantic rigor agnostic to specific application needs.

2. Credibility-Centered Extensions

Recent frameworks recognize that technical properties alone do not address the full adoption and impact of ontologies. A notable enhancement is the credibility-centered revision of F4OC, which introduces four quantifiable credibility indicators designed for operational relevance and community trust in fields such as cybersecurity (Leblanc et al., 1 Dec 2025):

1. Institutional Endorsement (IE): Binary indicator for citation or adoption within recognized standards (e.g., ISO 27005, MITRE ATT&CK).
2. Academic Recognition (AR): Binary indicator for peer-reviewed publication in high-tier venues.
3. Practitioner Validation (PV): Binary indicator confirming review and endorsement by independent practitioners with established credentials.
4. Industrial Maturity (IM): Trichotomous (0–3) scale reflecting demonstrable production deployment, case-paper documentation, and multi-party adoption.

A normalized aggregated credibility score can be computed:

$$C_{\text{total}} = w_1 \cdot IE + w_2 \cdot AR + w_3 \cdot PV + w_4 \cdot (IM/3)$$

where $w_1 + w_2 + w_3 + w_4 = 1$.

Ontologies are then mapped to credibility-based classes (e.g., Academic, Practical, Standardized, Industrial) and can be plotted in a four-dimensional "credibility cube" to assist selection according to stakeholder priorities.

3. Clustering- and ML-Based Classification Frameworks

Several classification frameworks leverage data-driven, machine learning, and graph-theoretic approaches to automate ontology classification and alignment:

• Predicate Similiarity and Hierarchical Clustering (MedTQ): MedTQ constructs a predicate similarity matrix for RDF/OWL ontologies at the schema level, combining shared-pattern and connection-pattern similarities (Shen et al., 2018). Clustering is performed via a divisive Hierarchical Predicate-based K-Means (HPKM), optimizing cluster number at each split using Neighborhood Silhouette Width (NSW). The process yields a topic hierarchy without manual $K$ selection, and each topic is instantiated as a SPARQL query pattern for further knowledge discovery. Evaluation on DrugBank ontology demonstrated the ability to segment complex ontological structures into cohesive, semantically meaningful clusters.
• NLP-Based Organizational Classification: Frameworks using pre-trained BERT architectures and fine-tuned classifiers have shown efficacy in aligning organizational entities to food-system and regulatory ontologies (Jiang et al., 2023). Textual snippets aggregated from web search are transformed into dense embeddings, and organizations are classified with respect to ontology categories (multi-label or multi-class) using cross-entropy loss functions. The output is integrated into knowledge graphs via RDF or OWL assertions.
• Relationship Classification and Ontology Construction from Corpus (Sci-OG): Sci-OG interleaves topic discovery via NER, relationship classification using hybrid deep encoders (fine-tuned SciBERT with Random Forest aggregation), and postprocessing with clustering, cycle removal, and expert curation (Pisu et al., 6 Aug 2025). Ontologies generated in this way achieve high F1 scores (e.g., 0.951 on a 21,649-pair dataset) and can be systematically extended (e.g., integration into CSO for cybersecurity research topics).

4. Graph-Based and Hybrid Approaches

Hybrid methods integrating ontology structure traversal and statistical learning are used for concept classification when explicit class mappings are not available or are ambiguous:

• In the financial domain, semantic concept classification leverages graph representations of ontologies (e.g., FIBO) and deterministic upward graph search to find hypernym labels for input concepts (Perdih et al., 2021). These methods may be augmented by word embedding models (e.g., word2vec, Random Forests), with a "ranking override" mechanism ensuring that ontology-derived labels dominate, but with ML-based ranking as a fallback.
• Generalization and integration include WordNet-based synonym expansion, distance-based scoring (e.g., $S_{\rm gen}(c,\ell)=1/(\mathrm{dist}_G(c,\ell)+1)$), and downstream population of knowledge graphs with ontology-aligned triples.

5. Parallelization and Scalability Frameworks

Efficient classification of large ontologies, especially in Description Logic, has motivated parallelization frameworks that treat existing DL reasoners as black-box subsumption engines (Quan et al., 2019). A parallel wrapper coordinates precomputing, task partitioning (work-stealing among threads), and taxonomy construction via shared atomic data structures. Speedups up to an order of magnitude have been reported for ontologies with up to ≈10,000 concepts. Key parameters influencing scalability include uniformity of subsumption test times, density and structure of logical axioms, and overhead from synchronization and scheduling.

6. Practical Guidelines and Selection Strategies

Ontology selection and design are increasingly driven by an overview of technical quality (e.g., well-groundedness, operationality) and credibility dimensions. Recommended practices (Leblanc et al., 1 Dec 2025) include:

• Applying pre-selection filters: prioritize ontologies that are well-grounded, reference-oriented, and operational. Gradually relax criteria if coverage is inadequate.
• Employing credibility thresholds based on intended use (e.g., requiring institutional endorsement in compliance-driven contexts).
• Weighting technical and credibility dimensions according to scenario, then visualizing candidates within a two-dimensional grid or higher-dimensional space for balanced selection.
• Periodic re-evaluation of industrial maturity and practitioner validation, with systematic maintenance in public repositories to track provenance, versioning, and evidence of adoption.

A plausible implication is that no single ontology is likely to satisfy all technical and credibility criteria; hybridization—composing a foundationally rigorous core with practical, industrially adopted modules—is common in real-world deployments.

7. Impact, Limitations, and Emerging Directions

Frameworks for ontology classification have facilitated rapid, principled comparison and integration of ontologies in diverse fields. However, there are notable limitations:

• Structural methods such as MedTQ ignore deep semantic relations (quantifiers, OWL axioms) and lack merging phases to recover from early mis-splits (Shen et al., 2018).
• Data-driven and hybrid techniques may be sensitive to domain drift, rare label sparsity, or fragile string-based mappings (Jiang et al., 2023, Perdih et al., 2021).
• Parallelization frameworks are bottlenecked by the underlying reasoner’s single-step performance for complex or unbalanced ontologies (Quan et al., 2019).

Emerging work emphasizes integrating lexical/semantic reasoning into similarity metrics, refining cluster validation, leveraging GPU or distributed architectures for large-scale classification, and expanding credibility frameworks to capture evolving evidence of operational success and trust within stakeholder communities.

In conclusion, systematic frameworks for ontologies classification—now spanning technical, social, and computational dimensions—are central to the robust, scalable, and trusted deployment of formal knowledge structures in advanced information systems.