Workflows in Linked Data (WiLD)

WiLD is an ontology for describing, specifying, monitoring, and executing workflows in Linked Data environments [1] [2]. It provides a structured vocabulary for representing workflow models, workflow instances, activities, execution order, data flow, and dependencies between workflow components [1]. WiLD focuses on workflows whose components interact through read-write Linked Data interfaces, making workflow descriptions machine-readable and suitable for semantic web applications [1] [2]. The ontology supports the formal representation of workflow structure and execution semantics, helping systems document, exchange, monitor, and reuse workflow descriptions in a consistent way [1]. By providing a linked data-based workflow model, WiLD can support reproducibility, automation, workflow sharing, and semantic integration of computational or data-processing processes [1] [2].

Example Usage: Represent a bioinformatics data-processing pipeline using WiLD terms to describe sequential workflow steps such as quality control, alignment, and variant calling, together with input datasets, execution order, tool or service invocations, intermediate data, and output data products. This enables workflow documentation, semantic search, monitoring, reproducibility, and reuse across Linked Data-based workflow systems [1] [2].

Metrics & Statistics

Graph Statistics

Total Nodes

50

Total Edges

91

Root Nodes

21

Leaf Nodes

9
Knowledge Coverage Statistics

Classes

16

Individuals

4

Properties

0
Hierarchical Metrics

Maximum Depth

2

Minimum Depth

0

Average Depth

0.58

Depth Variance

0.33
Breadth Metrics

Maximum Breadth

22

Minimum Breadth

2

Average Breadth

15.00

Breadth Variance

84.67
LLMs4OL Dataset Statistics

Term Types

4

Taxonomic Relations

9

Non-taxonomic Relations

4

Average Terms per Type

2.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import WiLD

ontology = WiLD()
ontology.load("path/to/WiLD-ontology.ttl")

# 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