Ontology Complexity Score Calculator
ANA›Life Services Authority›National Calculator Authority›Ontology Complexity Score Calculator
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Ontology Complexity Score Calculator
Calculates a composite Ontology Complexity Score (OCS) based on the number of classes, properties, axioms, hierarchy depth, and instance count. Useful for evaluating ontology maintainability and reasoning overhead.
Number of Classes (C)
Number of Properties (P) — object + datatype
Number of Axioms (A)
Maximum Hierarchy Depth (D)
Number of Instances / Individuals (I)
Number of Imported Modules / Ontologies (M)
Calculate Fill in the fields above and click Calculate.
function ontCalc() { var resultDiv = document.getElementById('ont-result');
var C = parseFloat(document.getElementById('ont-classes').value); var P = parseFloat(document.getElementById('ont-properties').value); var A = parseFloat(document.getElementById('ont-axioms').value); var D = parseFloat(document.getElementById('ont-depth').value); var I = parseFloat(document.getElementById('ont-instances').value); var M = parseFloat(document.getElementById('ont-modules').value);
// Validation if (isNaN(C) || C ⚠ Number of Classes must be at least 1.'; return; } if (isNaN(P) || P ⚠ Number of Properties must be 0 or greater.'; return; } if (isNaN(A) || A ⚠ Number of Axioms must be 0 or greater.'; return; } if (isNaN(D) || D ⚠ Maximum Hierarchy Depth must be at least 1.'; return; } if (isNaN(I) || I ⚠ Number of Instances must be 0 or greater.'; return; } if (isNaN(M) || M ⚠ Number of Imported Modules must be 0 or greater.'; return; }
// --- Core metrics ---
// Richness Ratio: properties per class var RR = P / C;
// Axiom Density: axioms per class var AD = A / C;
// Instance Ratio: instances per class (log-scaled to avoid dominance) var IR = (I > 0) ? Math.log10(1 + I / C) : 0;
// Module Factor: each imported module adds integration complexity var MF = 1 + 0.15 * M;
// Depth Factor: deeper hierarchies increase reasoning complexity var DF = Math.log2(1 + D);
// Ontology Complexity Score (OCS) // OCS = (log10(C) * DF * MF) + (w1 * RR) + (w2 * AD) + (w3 * IR) // Weights: w1=1.5 (properties matter), w2=1.0 (axioms baseline), w3=0.5 (instances less structural) var w1 = 1.5, w2 = 1.0, w3 = 0.5; var OCS = (Math.log10(C) * DF * MF) + (w1 * RR) + (w2 * AD) + (w3 * IR);
// Classify complexity tier var tier, tierColor, tierAdvice; if (OCS ' + OCS.toFixed(3) + '' + 'Complexity Tier: ' + tier + '' + '' + 'MetricValue' + 'Richness Ratio (P/C)' + RR.toFixed(4) + '' + 'Axiom Density (A/C)' + AD.toFixed(4) + '' + 'Instance Ratio log₁₀(1 + I/C)' + IR.toFixed(4) + '' + 'Depth Factor log₂(1 + D)' + DF.toFixed(4) + '' + 'Module Factor (1 + 0.15·M)' + MF.toFixed(4) + '' + '' + '' + tierAdvice + ''; }
#### Formula
OCS = ( log₁₀(C) × DF × MF ) + ( 1.5 × RR ) + ( 1.0 × AD ) + ( 0.5 × IR )
Where:
- C = Number of Classes
- RR = Richness Ratio = P / C (properties per class)
- AD = Axiom Density = A / C (axioms per class)
- IR = Instance Ratio = log₁₀(1 + I/C) (log-scaled instances per class)
- DF = Depth Factor = log₂(1 + D) (penalises deep hierarchies)
- MF = Module Factor = 1 + 0.15 × M (each imported module adds 15% overhead)
Complexity Tiers: Low (<5) | Moderate (5–15) | High (15–30) | Very High (≥30)
#### Assumptions & References
- References: Tartir et al. (2005) OntoQA; Gangemi et al. (2006) Modelling Ontology Evaluation; Brank et al. (2005) A Survey of Ontology Evaluation Techniques.
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