Academic Institution Internal Structure Ontology (AIISO)

AIISO is a specialized ontology for describing and formalizing the internal organizational structures, hierarchies, and administrative units of academic institutions including universities, colleges, and research centers. It provides standardized vocabulary for representing academic departments, faculties, schools, research groups, and their relationships within an institution’s organizational hierarchy. AIISO is designed for integration with complementary ontologies including Participation (for describing role participation), FOAF (for person information), and aiiso-roles (for person roles within institutions). The ontology enables semantic representation of academic organizational information, supporting institutional data integration, organizational mapping, and staff/student management systems. AIISO facilitates interoperability in academic information systems and institutional repositories by providing standardized semantic definitions of academic structures.

Example Usage: Represent an academic institution’s structure with AIISO terms for faculties (Faculty of Science, Faculty of Engineering), departments (Computer Science Department, Physics Department), research groups, and their organizational relationships and hierarchies.

Metrics & Statistics

Graph Statistics

Total Nodes

119

Total Edges

247

Root Nodes

8

Leaf Nodes

54

Knowledge Coverage Statistics

Classes

22

Individuals

0

Properties

0

Hierarchical Metrics

Maximum Depth

2

Minimum Depth

0

Average Depth

0.89

Depth Variance

0.47

Breadth Metrics

Maximum Breadth

14

Minimum Breadth

5

Average Breadth

9.00

Breadth Variance

14.00

LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

13

Non-taxonomic Relations

3

Average Terms per Type

0.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import AIISO

ontology = AIISO()
ontology.load("path/to/AIISO-ontology.rdf")

# Extract datasets
data = ontology.extract()

# Access specific relations
term_types = data.term_typings
taxonomic_relations = data.type_taxonomies
non_taxonomic_relations = data.type_non_taxonomic_relations