Metrics¶

The Analyzer class in OntoLearner provides a unified interface for computing ontology metrics, which can be divided into two main categories: Topology Metrics (capture the structural characteristics of the ontology graph) and Dataset Metrics (assess the quality and distribution of the extracted learning datasets). Additionally, a complexity score can be derived from these metrics to summarize the overall ontology richness and complexity.

Topology Metrics¶

Topology metrics describe the structure and organization of an ontology. The Analyzer computes the following key metrics:

Total nodes (total_nodes): Total number of nodes in the ontology graph.
Total edges (total_edges): Total number of edges representing relations between nodes.
Root nodes (num_root_nodes): Nodes with no incoming edges, representing top-level concepts.
Leaf nodes (num_leaf_nodes): Nodes with no outgoing edges, representing bottom-level concepts.
Classes (num_classes): Number of distinct ontology classes.
Properties (num_properties): Number of distinct properties (object or datatype properties).
Individuals (num_individuals): Number of instances associated with classes.
Depth metrics:
- max_depth: Maximum hierarchical depth in the ontology.
- min_depth: Minimum hierarchical depth.
- avg_depth: Average hierarchical depth across all nodes.
- depth_variance: Variance of depth distribution.
Breadth metrics:
- max_breadth: Maximum number of nodes at any single hierarchy level.
- min_breadth: Minimum number of nodes at any hierarchy level.
- avg_breadth: Average number of nodes per hierarchy level.
- breadth_variance: Variance of breadth distribution.

Dataset Metrics¶

Dataset metrics evaluate the characteristics of machine-learning datasets extracted from the ontology. These metrics include:

Number of term-type mappings (num_term_types): Number of terms associated with types.
Number of taxonomic (is-a) relations (num_taxonomic_relations): Count of hierarchical relations.
Number of non-taxonomic relations (num_non_taxonomic_relations): Count of semantic associations not in the hierarchy.
Average terms per type (avg_terms): Measures dataset balance across classes.

Complexity Score¶

The complexity score combines topology and dataset metrics into a single normalized score in [0, 1]. First, metrics are log-normalized and weighted by category:

Metric Category	Example Metrics	Weight
Graph structure	`total_nodes`, `total_edges`, `num_root_nodes`, `num_leaf_nodes`	0.3
Knowledge coverage	`num_classes`, `num_properties`, `num_individuals`	0.25
Hierarchy	`max_depth`, `min_depth`, `avg_depth`, `depth_variance`	0.10
Breadth	`max_breadth`, `min_breadth`, `avg_breadth`, `breadth_variance`	0.20
Dataset (LLMs4OL)	`num_term_types`, `num_taxonomic_relations`, `num_non_taxonomic_relations`, `avg_terms`	0.15

Next, the weighted sum of metrics is passed through a logistic function to normalize the final complexity score.

Example Usage¶

Here is a simple example demonstrating how to compute metrics and complexity for an ontology:

from ontolearner.tools import Analyzer
from ontolearner.ontology import Wine

# Step 1 — Load ontology
ontology = Wine()
ontology.build_graph()

# Step 2 — Create the analyzer
analyzer = Analyzer()

# Step 3 — Compute topology and dataset metrics
topology_metrics = analyzer.compute_topology_metrics(ontology)
dataset_metrics = analyzer.compute_dataset_metrics(ontology)

# Step 4 — Compute overall complexity score
complexity_score = analyzer.compute_complexity_score(
        topology_metrics=topology_metrics,
        dataset_metrics=dataset_metrics
        )
# Step 5 — Display results
print("Topology Metrics:", topology_metrics)
print("Dataset Metrics:", dataset_metrics)
print("Ontology Complexity Score:", complexity_score)

This workflow allows ontology engineers and researchers to quantify structural quality, dataset richness, and overall complexity, providing actionable insights for ontology evaluation, benchmarking, and improvement.