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Merge pull request #171 from sciknoworg/dev
Textual datasets integration and complete list of ontologies
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‎docs/source/benchmarking/benchmark.rst‎

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materials_science/crystallography.rst
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materials_science/cif_core.rst
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materials_science/atomistic.rst
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materials_science/disco.rst
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materials_science/mambo.rst
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materials_science/lpbfo.rst
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materials_science/am_ontology.rst
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materials_science/asmo.rst
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materials_science/bmo.rst
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materials_science/cdco.rst
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materials_science/cmso.rst
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materials_science/deb.rst
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materials_science/dsim.rst
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materials_science/ldo.rst
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materials_science/m4i.rst
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materials_science/mat.rst
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materials_science/matvoc.rst
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materials_science/mds.rst
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materials_science/mechanical_testing.rst
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materials_science/microstructure.rst
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materials_science/mol-brinell.rst
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materials_science/moltensile.rst
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materials_science/mseo.rst
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materials_science/msle.rst
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materials_science/npo.rst
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materials_science/oeo.rst
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materials_science/oie-materials.rst
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materials_science/ontorule.rst
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materials_science/opmw.rst
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materials_science/periodic_table.rst
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materials_science/photovoltaics.rst
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materials_science/pldo.rst
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materials_science/pmdco.rst
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materials_science/podo.rst
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materials_science/prima.rst
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materials_science/qudv.rst
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materials_science/sp-document.rst
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materials_science/sp-workflow.rst
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materials_science/vimo.rst
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materials_science/wild.rst
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Social Sciences
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===============

‎docs/source/benchmarking/materials_science/am_ontology.rst‎

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Additive Manufacturing Ontology (AMOntology)
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=============================================
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Overview
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-----------------
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The AM ontology has been developed following two major milestones. The ontology developed within the first milestone
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includes AMProcessOntology, ModelOntology and AMOntology files. AMProcessOntology contains the set of entities
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used to capture knowledge about additive manufacturing processes. ModelOntology contains the set of entities
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used to capture knowledge about modeling concepts that represent (possibly) multi-physics multi-scale processes.
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AMOntology uses AMProcessOntology and ModelOntology files to describe entities that capture knowledge
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about characteristics of computational models for AM processes.
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:Domain: Materials Science & Engineering
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:Category: Manufacturing
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:Current Version:
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:Last Updated:
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:Producer:
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:License:
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:Format: Turtle
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:Download: `AMOntology Homepage <https://github.com/iassouroko/AMontology>`_
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:Documentation: `AMOntology Documentation <https://github.com/iassouroko/AMontology>`_
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Base Metrics
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---------------
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- Classes:
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- Individuals:
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- Properties:
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Graph Metrics
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------------------
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- **Total Nodes**: 900
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- **Root Nodes**: 71
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- **Leaf Nodes**: 99
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- **Maximum Depth**: 24
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- **Edges**: 2299
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Dataset Statistics
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-------------------
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Generated Benchmarks:
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- **Term Types**: 59
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- **Taxonomic Relations**: 1603
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- **Non-taxonomic Relations**: 24
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- **Average Terms per Type**: 1.48
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Usage Example
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------------------
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.. code-block:: python
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from ontolearner.ontology import AMOntology
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# Initialize and load ontology
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ontology = AMOntology()
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ontology.load("path/to/am_ontology.ttl")
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# Extract datasets
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data = ontology.extract()
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# Access specific relations
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term_types = data.term_typings
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taxonomic_relations = data.type_taxonomies
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non_taxonomic_relations = data.type_non_taxonomic_relations

‎docs/source/benchmarking/materials_science/asmo.rst‎

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Atomistic Simulation Methods Ontology (ASMO)
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===================
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Overview
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-----------------
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ASMO is an ontology that aims to define the concepts needed to describe commonly
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used atomic scale simulation methods, i.e. density functional theory, molecular dynamics,
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Monte Carlo methods, etc. ASMO uses the Provenance Ontology (PROV-O) to describe the simulation process.
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:Domain: Materials Science and Engineering
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:Category: Materials Science
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:Current Version: 1.0.0
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:Last Updated:
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:Creator: https://orcid.org/0000-0001-7564-7990
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:License: Creative Commons Attribution 4.0 International (CC BY 4.0)
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:Format: OWL
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:Download: `ASMO Homepage <https://github.com/OCDO/asmo?tab=readme-ov-file#atomistic-simulation-methods-ontology-asmo>`_
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:Documentation: `ASMO Documentation <https://github.com/OCDO/asmo?tab=readme-ov-file#atomistic-simulation-methods-ontology-asmo>`_
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Base Metrics
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---------------
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- Classes:
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- Individuals:
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- Properties:
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Graph Metrics
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------------------
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- **Total Nodes**: 588
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- **Root Nodes**: 23
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- **Leaf Nodes**: 360
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- **Maximum Depth**: 6
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- **Edges**: 1058
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Dataset Statistics
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-------------------
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Generated Benchmarks:
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- **Term Types**: 30
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- **Taxonomic Relations**: 99
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- **Non-taxonomic Relations**: 2
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- **Average Terms per Type**: 0.94
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Usage Example
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------------------
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.. code-block:: python
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from ontolearner.ontology import ASMO
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# Initialize and load ontology
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ontology = ASMO()
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ontology.load("path/to/asmo.owl")
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# Extract datasets
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data = ontology.extract()
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# Access specific relations
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term_types = data.term_typings
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taxonomic_relations = data.type_taxonomies
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non_taxonomic_relations = data.type_non_taxonomic_relations

‎docs/source/benchmarking/materials_science/bmo.rst‎

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Building Material Ontology (BMO)
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================================
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Overview
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-----------------
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Building Material Ontology defines the main concepts of building material,
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types, layers, and properties.
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:Domain: Materials Science & Engineering
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:Category: Materials
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:Current Version: 0.1
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:Last Updated: 2019-12-10
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:Producer: Janakiram Karlapudi (janakiram.karlapudi@tu-dresden.de), Prathap Valluru (prathap.valluru@tu-dresden.de)
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:License: Creative Commons Attribution 4.0 International (CC BY 4.0)
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:Format: TTL, OWL, RDF/XML
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:Download: `BMO Homepage <https://matportal.org/ontologies/BUILDMAT>`_
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:Documentation: `BMO Documentation <{https://matportal.org/ontologies/BUILDMAT>`_
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Base Metrics
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---------------
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- Classes: 26
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- Individuals: 12
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- Properties: 63
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Graph Metrics
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------------------
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- **Total Nodes**: 203
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- **Root Nodes**: 83
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- **Leaf Nodes**: 68
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- **Maximum Depth**: 7
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- **Edges**: 420
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Dataset Statistics
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-------------------
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Generated Benchmarks:
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- **Term Types**: 12
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- **Taxonomic Relations**: 20
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- **Non-taxonomic Relations**: 0
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- **Average Terms per Type**: 0.67
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Usage Example
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------------------
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.. code-block:: python
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from ontolearner.ontology import BMO
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# Initialize and load ontology
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ontology = BMO()
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ontology.load("path/to/bmo.ttl")
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# Extract datasets
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data = ontology.extract()
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# Access specific relations
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term_types = data.term_typings
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taxonomic_relations = data.type_taxonomies
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non_taxonomic_relations = data.type_non_taxonomic_relations

‎docs/source/benchmarking/materials_science/cdco.rst‎

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Crystallographic Defect Core Ontology (CDCO)
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=============================================
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Overview
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-----------------
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CDCO defines the common terminology shared across all types of crystallographic defects,
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providing a unified framework for data integration in materials science.
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:Domain: Material Science and Engineering
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:Category: Materials Science Ontology
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:Current Version: 1.0.0
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:Last Updated:
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:Creator: https://orcid.org/0000-0001-7564-7990
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:License: Creative Commons Attribution 4.0 International (CC BY 4.0)
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:Format: OWL
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:Download: `CDCO Homepage <https://github.com/OCDO/cdco>`_
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:Documentation: `CDCO Documentation <https://github.com/OCDO/cdco>`_
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Base Metrics
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---------------
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- Classes:
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- Individuals:
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- Properties:
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Graph Metrics
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------------------
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- **Total Nodes**: 85
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- **Root Nodes**: 8
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- **Leaf Nodes**: 53
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- **Maximum Depth**: 1
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- **Edges**: 123
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Dataset Statistics
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-------------------
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Generated Benchmarks:
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- **Term Types**: 0
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- **Taxonomic Relations**: 4
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- **Non-taxonomic Relations**: 0
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- **Average Terms per Type**: 0
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Usage Example
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------------------
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.. code-block:: python
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from ontolearner.ontology import CDCO
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# Initialize and load ontology
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ontology = CDCO()
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ontology.load("path/to/cdco.ttl")
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# Extract datasets
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data = ontology.extract()
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# Access specific relations
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term_types = data.term_typings
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taxonomic_relations = data.type_taxonomies
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non_taxonomic_relations = data.type_non_taxonomic_relations

‎docs/source/benchmarking/materials_science/cmso.rst‎

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Computational Material Sample Ontology (CMSO)
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===================
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Overview
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-----------------
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CMSO is an ontology that aims to describe computational materials science samples (or structures),
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including crystalline defects. Initially focusing on the description at the atomic scale.
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:Domain: Materials Science and Engineering
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:Category: Materials Science
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:Current Version: 0.0.1
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:Last Updated:
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:Creator: https://orcid.org/0000-0001-7564-7990
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:License: Creative Commons Attribution 4.0 International (CC BY 4.0)
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:Format: OWL
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:Download: `CMSO Homepage <https://github.com/OCDO/cmso/tree/main>`_
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:Documentation: `CMSO Documentation <https://github.com/OCDO/cmso/tree/main>`_
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Base Metrics
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---------------
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- Classes:
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- Individuals:
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- Properties:
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Graph Metrics
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------------------
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- **Total Nodes**: 347
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- **Root Nodes**: 40
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- **Leaf Nodes**: 192
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- **Maximum Depth**: 2
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- **Edges**: 556
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Dataset Statistics
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-------------------
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Generated Benchmarks:
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- **Term Types**: 0
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- **Taxonomic Relations**: 22
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- **Non-taxonomic Relations**: 1
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- **Average Terms per Type**: 0
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Usage Example
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------------------
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.. code-block:: python
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from ontolearner.ontology import CMSO
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# Initialize and load ontology
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ontology = CMSO()
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ontology.load("path/to/cmso.owl")
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# Extract datasets
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data = ontology.extract()
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# Access specific relations
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term_types = data.term_typings
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taxonomic_relations = data.type_taxonomies
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non_taxonomic_relations = data.type_non_taxonomic_relations

‎docs/source/benchmarking/materials_science/deb.rst‎

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Devices, Experimental scaffolds and Biomaterials Ontology (DEB)
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===============================================================
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Overview
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-----------------
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The devices, experimental scaffolds, and biomaterials ontology (DEB) is an open resource
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for organizing information about biomaterials, their design, manufacture, and biological testing.
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It was developed using text analysis for identifying ontology terms from a biomaterials gold standard corpus,
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systematically curated to represent the domain's lexicon. Topics covered were validated by members
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of the biomaterials research community.
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:Domain: Materials Science & Engineering
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:Category: Materials Science
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:Current Version: 06/2021
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:Last Updated: Jun 2, 2021
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:Producer: Osnat Hakimi
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:License: GPL-3.0
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:Format: OWL/XML
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:Download: `DEB Homepage <https://github.com/ProjectDebbie/Ontology_DEB>`_
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:Documentation: `DEB Documentation <https://github.com/ProjectDebbie/Ontology_DEB>`_
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Base Metrics
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---------------
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- Classes:
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- Individuals:
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- Properties:
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Graph Metrics
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------------------
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- **Total Nodes**: 1081
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- **Root Nodes**: 533
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- **Leaf Nodes**: 278
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- **Maximum Depth**: 6
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- **Edges**: 2354
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Dataset Statistics
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-------------------
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Generated Benchmarks:
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- **Term Types**: 0
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- **Taxonomic Relations**: 820
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- **Non-taxonomic Relations**: 8
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- **Average Terms per Type**: 0
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Usage Example
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------------------
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.. code-block:: python
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from ontolearner.ontology import DEB
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# Initialize and load ontology
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ontology = DEB()
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ontology.load("path/to/deb.ttl")
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# Extract datasets
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data = ontology.extract()
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# Access specific relations
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term_types = data.term_typings
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taxonomic_relations = data.type_taxonomies
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non_taxonomic_relations = data.type_non_taxonomic_relations

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