Software Ontology (SWO)

The Software Ontology (SWO) is a resource for describing software tools, their types, tasks, versions, provenance, and associated data [1]. It contains detailed information on licensing and formats as well as software applications themselves, mainly, but not limited to, the bioinformatics community [1]. It provides a structured vocabulary for representing software tools, supporting research in software description, reproducibility, data analysis, curation, and digital preservation [1].

The ontology employs a class-based modeling approach, defining classes for different types of software tools, tasks, versions, licenses, formats, and associated data, along with properties to describe their characteristics and interactions [1]. Hierarchies are used to organize software-related concepts into categories, enabling efficient retrieval, comparison, and analysis [1]. SWO supports the integration of software metadata from various sources, promoting interoperability and data-driven research in software management and biomedical data analysis [1].

Typical applications of SWO include software tool annotation, software cataloging, reproducibility support, biomedical data analysis documentation, digital preservation, and integration of software metadata across repositories and research platforms [1]. By providing a standardized vocabulary and framework, SWO enhances interoperability, reuse, and knowledge discovery in the field of software management [1].

Example Usage: Annotate a software tool with SWO terms to specify its tool type, task, version, license, input and output data formats, provenance, and associated data. This enables semantic search, reproducibility tracking, and integration with software management platforms [1].

Metrics & Statistics

Graph Statistics

Total Nodes

11581

Total Edges

33570

Root Nodes

177

Leaf Nodes

3150
Knowledge Coverage Statistics

Classes

2746

Individuals

443

Properties

165
Hierarchical Metrics

Maximum Depth

13

Minimum Depth

0

Average Depth

3.07

Depth Variance

5.30
Breadth Metrics

Maximum Breadth

392

Minimum Breadth

1

Average Breadth

132.93

Breadth Variance

17222.21
LLMs4OL Dataset Statistics

Term Types

440

Taxonomic Relations

5852

Non-taxonomic Relations

612

Average Terms per Type

8.30

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import SWO

ontology = SWO()
ontology.load("path/to/SWO-ontology.owl")

# 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

References