BIO: A vocabulary for biographical information (BIO)

The BIO vocabulary is a domain ontology for describing biographical information about people, their backgrounds, and genealogical relationships. It models a person’s life as a series of interconnected key events (birth, marriage, employment, death, etc.), providing a framework for weaving together biographical and genealogical data. BIO defines an event-centric approach, supplying a set of core event types and properties that are person-centric, making it suitable for representing life histories, family trees, and career trajectories. The ontology is designed for extensibility, allowing other vocabularies to build upon its event framework for specialized use cases. BIO is widely used in digital humanities, genealogy platforms, and social history research to enable semantic annotation, data integration, and advanced queries on biographical datasets. By providing a standardized vocabulary, BIO facilitates interoperability and knowledge sharing across biographical and genealogical information systems.

Example Usage: Annotate a genealogy database with BIO terms to describe a person’s birth, marriage, and employment events, linking them to dates, places, and related individuals for semantic search and family history visualization.

Metrics & Statistics

Graph Statistics

Total Nodes

326

Total Edges

816

Root Nodes

16

Leaf Nodes

187

Knowledge Coverage Statistics

Classes

44

Individuals

1

Properties

30

Hierarchical Metrics

Maximum Depth

7

Minimum Depth

0

Average Depth

2.24

Depth Variance

5.64

Breadth Metrics

Maximum Breadth

30

Minimum Breadth

1

Average Breadth

8.75

Breadth Variance

88.44

LLMs4OL Dataset Statistics

Term Types

1

Taxonomic Relations

58

Non-taxonomic Relations

0

Average Terms per Type

1.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import BIO

ontology = BIO()
ontology.load("path/to/BIO-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