Metadata for Intelligent Engineering (Metadata4Ing)

The Metadata4Ing ontology provides a semantic framework for describing research data and the processes through which those data are generated, with a particular focus on engineering sciences and related disciplines [1] [2]. It supports the representation of experiments, observations, simulations, objects of investigation, sample and data manipulation procedures, generated data files, and the personal or institutional roles involved in a research activity [1] [2].

Metadata4Ing follows a modular and extensible modeling approach in which research processes can be described through classes and relationships for methods, tools, variables, organizations, people, roles, and generated outputs [1]. This allows the ontology to capture not only the resulting research data, but also the contextual and procedural information needed to understand how those data were produced [1] [2]. The ontology therefore supports machine-readable documentation of research workflows and facilitates interpretation, integration, and reuse of engineering research data [1].

Metadata4Ing is intended for research data management and semantic description of scientific activities, including experimental, observational, simulation, and data-processing workflows [1] [2]. Its structured representation of data-generation processes can support provenance documentation, interoperability between research infrastructures, FAIR-oriented data publication, and reuse of research information across engineering and neighbouring scientific domains [1] [2].

Example Usage: Annotate an engineering experiment with Metadata4Ing terms to describe the object of investigation, the experimental or simulation method, tools and configurations used, variables and parameters, generated data files, and the persons or institutions responsible for different parts of the process. This provides a machine-readable description of how the research data were generated and supports provenance tracking, interpretation, and reuse [1] [2].

Metrics & Statistics

Graph Statistics

Total Nodes

1032

Total Edges

1517

Root Nodes

109

Leaf Nodes

731
Knowledge Coverage Statistics

Classes

48

Individuals

47

Properties

100
Hierarchical Metrics

Maximum Depth

7

Minimum Depth

0

Average Depth

1.54

Depth Variance

1.36
Breadth Metrics

Maximum Breadth

413

Minimum Breadth

4

Average Breadth

109.75

Breadth Variance

18099.19
LLMs4OL Dataset Statistics

Term Types

37

Taxonomic Relations

44

Non-taxonomic Relations

6

Average Terms per Type

9.25

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import Metadata4Ing

ontology = Metadata4Ing()
ontology.load("path/to/Metadata4Ing-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