Update chemistry domain with 15 ontologies

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README.md

@@ -0,0 +1,50 @@
+
+---
+license: mit
+language:
+- en
+tags:
+- OntoLearner
+- ontology-learning
+- chemistry
+pretty_name: Agricultural
+---
+<div>
+  <img  src="https://raw.githubusercontent.com/sciknoworg/OntoLearner/main/images/logo.png"  alt="OntoLearner"
+    style="display: block; margin: 0 auto; width: 500px; height: auto;">
+  <h1 style="text-align: center; margin-top: 1em;">Chemistry Domain Ontologies</h1>
+</div>
+
+## Overview
+The domain of chemistry ontologies encompasses the structured representation and formalization of chemical knowledge, including entities, reactions, processes, and methodologies. It plays a critical role in facilitating interoperability, data integration, and advanced computational modeling across diverse chemical disciplines. By providing a standardized framework, these ontologies enhance the precision and efficiency of information exchange and support the advancement of research and innovation in the chemical sciences.
+
+## Ontologies
+| Ontology ID | Full Name | Classes | Properties | Last Updated |
+|-------------|-----------|---------|------------|--------------|
+| AFO | Allotrope Foundation Ontology (AFO) | 3871 | 318 | 2024-06-28|
+| ChEBI | Chemical Entities of Biological Interest (ChEBI) | 220816 | 10 | 01/01/2025|
+| CHEMINF | Chemical Information Ontology (CHEMINF) | 358 | 52 | None|
+| CHIRO | CHEBI Integrated Role Ontology (CHIRO) | 13930 | 15 | 2015-11-23|
+| ChMO | Chemical Methods Ontology (ChMO) | 3202 | 27 | 2022-04-19|
+| FIX | FIX Ontology (FIX) | 1163 | 5 | 2020-04-13|
+| MassSpectrometry | Mass Spectrometry Ontology (MassSpectrometry) | 3636 | 12 | 12:02:2025|
+| MOP | Molecular Process Ontology (MOP) | 3717 | 11 | 2022-05-11|
+| NMRCV | Nuclear Magnetic Resonance Controlled Vocabulary (NMRCV) | 757 | 0 | 2017-10-19|
+| OntoKin | Chemical Kinetics Ontology (OntoKin) | 83 | 136 | 08 February 2022|
+| PROCO | PROcess Chemistry Ontology (PROCO) | 970 | 61 | 04-14-2022|
+| PSIMOD | Proteomics Standards Initiative (PSI) Protein Modifications Ontology (PSI-MOD) | 2098 | 4 | 2022-06-13|
+| REX | Physico-chemical process ontology (REX) | 552 | 6 | 2025-03-11|
+| RXNO | Reaction Ontology (RXNO) | 1109 | 14 | 2021-12-16|
+| VIBSO | Vibrational Spectroscopy Ontology (VIBSO) | 598 | 53 | 2024-09-23|
+
+## Dataset Files
+Each ontology directory contains the following files:
+1. `<ontology_id>.<format>` - The original ontology file
+2. `term_typings.json` - Dataset of term to type mappings
+3. `taxonomies.json` - Dataset of taxonomic relations
+4. `non_taxonomic_relations.json` - Dataset of non-taxonomic relations
+5. `<ontology_id>.rst` - Documentation describing the ontology
+
+## Usage
+These datasets are intended for ontology learning research and applications.
+    

afo/afo.rst

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+Allotrope Foundation Ontology (AFO)
+========================================================================================================================
+
+Overview
+--------
+The AFO is an ontology suite that provides a standard vocabulary and semantic model
+for the representation of laboratory analytical processes. The AFO suite is aligned at the upper layer
+to the Basic Formal Ontology (BFO). The core domains modeled include, Equipment, Material, Process, and Results.
+This artifact contains all triples of Allotrope Foundation Merged Without QUDT Ontology Suite (REC/2023/12)
+together with triples inferred with HermiT.
+
+:Domain: Chemistry
+:Category: Laboratory Analytical Processes
+:Current Version: 2024-06
+:Last Updated: 2024-06-28
+:Creator: Allotrope Foundation
+:License: CC BY 4.0
+:Format: TTL
+:Download: `Allotrope Foundation Ontology (AFO) Homepage <https://terminology.tib.eu/ts/ontologies/AFO>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 15547
+    - **Total Edges**: 36699
+    - **Root Nodes**: 142
+    - **Leaf Nodes**: 8003
+
+Knowledge coverage
+------------------
+    - Classes: 3871
+    - Individuals: 38
+    - Properties: 318
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 24
+    - **Minimum Depth**: 0
+    - **Average Depth**: 5.14
+    - **Depth Variance**: 22.60
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 368
+    - **Minimum Breadth**: 1
+    - **Average Breadth**: 75.84
+    - **Breadth Variance**: 8251.25
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 38
+    - **Taxonomic Relations**: 9889
+    - **Non-taxonomic Relations**: 34
+    - **Average Terms per Type**: 3.45
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import AFO
+
+    # Initialize and load ontology
+    ontology = AFO()
+    ontology.load("path/to/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

chebi/chebi.owl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3df51b665dfa54499a41482a2456851983f50b709cefb4c5e9aaeb71a95e6814
+size 814872764

chebi/chebi.rst

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+Chemical Entities of Biological Interest (ChEBI)
+========================================================================================================================
+
+Overview
+--------
+Chemical Entities of Biological Interest (ChEBI) is a dictionary of molecular entities
+focused on ‘small’ chemical compounds. The term ‘molecular entity’ refers to any constitutionally
+or isotopically distinct atom, molecule, ion, ion pair, radical, radical ion, complex, conformer, etc.,
+identifiable as a separately distinguishable entity. The molecular entities in question
+are either products of nature or synthetic products used to intervene in the processes of living organisms.
+ChEBI incorporates an ontological classification, whereby the relationships between molecular entities
+or classes of entities and their parents and/or children are specified.
+
+:Domain: Chemistry
+:Category: Chemical Entities
+:Current Version: 239
+:Last Updated: 01/01/2025
+:Creator: None
+:License: Creative Commons 4.0
+:Format: OWL, OBO, JSON
+:Download: `Chemical Entities of Biological Interest (ChEBI) Homepage <https://www.ebi.ac.uk/chebi/>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 2433610
+    - **Total Edges**: 6913389
+    - **Root Nodes**: 609907
+    - **Leaf Nodes**: 1528418
+
+Knowledge coverage
+------------------
+    - Classes: 220816
+    - Individuals: 0
+    - Properties: 10
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 6
+    - **Minimum Depth**: 0
+    - **Average Depth**: 1.14
+    - **Depth Variance**: 0.69
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 908127
+    - **Minimum Breadth**: 26
+    - **Average Breadth**: 310545.00
+    - **Breadth Variance**: 135103408992.57
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 0
+    - **Taxonomic Relations**: 1200620
+    - **Non-taxonomic Relations**: 18607
+    - **Average Terms per Type**: 0.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import ChEBI
+
+    # Initialize and load ontology
+    ontology = ChEBI()
+    ontology.load("path/to/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

cheminf/cheminf.rst

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+Chemical Information Ontology (CHEMINF)
+========================================================================================================================
+
+Overview
+--------
+The chemical information ontology (cheminf) describes information entities about chemical entities.
+It provides qualitative and quantitative attributes to richly describe chemicals.
+Includes terms for the descriptors commonly used in cheminformatics software applications
+and the algorithms which generate them.
+
+:Domain: Chemistry
+:Category: Chemistry
+:Current Version: 2.1.0
+:Last Updated: None
+:Creator: Egon Willighagen, Nina Jeliazkova, Ola Spjuth, Valery Tkachenko
+:License: Creative Commons 1.0
+:Format: OWL
+:Download: `Chemical Information Ontology (CHEMINF) Homepage <https://terminology.tib.eu/ts/ontologies/CHEMINF>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 1467
+    - **Total Edges**: 2837
+    - **Root Nodes**: 213
+    - **Leaf Nodes**: 435
+
+Knowledge coverage
+------------------
+    - Classes: 358
+    - Individuals: 0
+    - Properties: 52
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 16
+    - **Minimum Depth**: 0
+    - **Average Depth**: 1.73
+    - **Depth Variance**: 9.21
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 213
+    - **Minimum Breadth**: 1
+    - **Average Breadth**: 29.24
+    - **Breadth Variance**: 3411.59
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 0
+    - **Taxonomic Relations**: 594
+    - **Non-taxonomic Relations**: 1
+    - **Average Terms per Type**: 0.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import CHEMINF
+
+    # Initialize and load ontology
+    ontology = CHEMINF()
+    ontology.load("path/to/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

chiro/chiro.rst

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+CHEBI Integrated Role Ontology (CHIRO)
+========================================================================================================================
+
+Overview
+--------
+CHEBI provides a distinct role hierarchy. Chemicals in the structural hierarchy are connected via a 'has role' relation.
+CHIRO provides links from these roles to useful other classes in other ontologies.
+This will allow direct connection between chemical structures (small molecules, drugs) and what they do.
+This could be formalized using 'capable of', in the same way Uberon and the Cell Ontology link structures to processes.
+
+:Domain: Chemistry
+:Category: Chemicals, Roles
+:Current Version: 2015-11-23
+:Last Updated: 2015-11-23
+:Creator: None
+:License: Creative Commons 1.0
+:Format: OWL
+:Download: `CHEBI Integrated Role Ontology (CHIRO) Homepage <https://terminology.tib.eu/ts/ontologies/chiro>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 81778
+    - **Total Edges**: 197071
+    - **Root Nodes**: 14636
+    - **Leaf Nodes**: 50439
+
+Knowledge coverage
+------------------
+    - Classes: 13930
+    - Individuals: 0
+    - Properties: 15
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 16
+    - **Minimum Depth**: 0
+    - **Average Depth**: 1.36
+    - **Depth Variance**: 1.13
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 34719
+    - **Minimum Breadth**: 2
+    - **Average Breadth**: 4620.24
+    - **Breadth Variance**: 105924794.30
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 0
+    - **Taxonomic Relations**: 27299
+    - **Non-taxonomic Relations**: 0
+    - **Average Terms per Type**: 0.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import CHIRO
+
+    # Initialize and load ontology
+    ontology = CHIRO()
+    ontology.load("path/to/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

chmo/chmo.rst

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+Chemical Methods Ontology (ChMO)
+========================================================================================================================
+
+Overview
+--------
+The Chemical Methods Ontology contains more than 3000 classes and describes methods used to:
+- collect data in chemical experiments, such as mass spectrometry and electron microscopy.
+- prepare and separate material for further analysis, such as sample ionisation, chromatography, and electrophoresis
+- synthesise materials, such as epitaxy and continuous vapour deposition It also describes the instruments used
+    in these experiments, such as mass spectrometers and chromatography columns and their outputs.
+
+:Domain: Chemistry
+:Category: Chemistry
+:Current Version: None
+:Last Updated: 2022-04-19
+:Creator: None
+:License: Creative Commons 4.0
+:Format: OWL, TTL, CSV, NT
+:Download: `Chemical Methods Ontology (ChMO) Homepage <https://github.com/rsc-ontologies/rsc-cmo>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 24075
+    - **Total Edges**: 44651
+    - **Root Nodes**: 3100
+    - **Leaf Nodes**: 17250
+
+Knowledge coverage
+------------------
+    - Classes: 3202
+    - Individuals: 0
+    - Properties: 27
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 7
+    - **Minimum Depth**: 0
+    - **Average Depth**: 1.49
+    - **Depth Variance**: 0.63
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 13439
+    - **Minimum Breadth**: 1
+    - **Average Breadth**: 2993.88
+    - **Breadth Variance**: 20855464.86
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 0
+    - **Taxonomic Relations**: 4268
+    - **Non-taxonomic Relations**: 114
+    - **Average Terms per Type**: 0.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import ChMO
+
+    # Initialize and load ontology
+    ontology = ChMO()
+    ontology.load("path/to/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

dataset_infos.json

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+{
+  "name": "Ontology Domain: Chemistry",
+  "description": "Dataset containing ontologies and processed data for the Chemistry domain",
+  "license": "mixed",
+  "tags": [
+    "ontology",
+    "chemistry",
+    "knowledge-graph"
+  ],
+  "ontologies": [
+    "AFO",
+    "ChEBI",
+    "CHEMINF",
+    "CHIRO",
+    "ChMO",
+    "FIX",
+    "MassSpectrometry",
+    "MOP",
+    "NMRCV",
+    "OntoKin",
+    "PROCO",
+    "PSIMOD",
+    "REX",
+    "RXNO",
+    "VIBSO"
+  ]
+}

fix/fix.rst

@@ -0,0 +1,68 @@
+FIX Ontology (FIX)
+========================================================================================================================
+
+Overview
+--------
+An ontology of physico-chemical methods and properties.
+
+:Domain: Chemistry
+:Category: Chemicals, Properties
+:Current Version: 2020-04-13
+:Last Updated: 2020-04-13
+:Creator: None
+:License: None
+:Format: OWL
+:Download: `FIX Ontology (FIX) Homepage <https://terminology.tib.eu/ts/ontologies/FIX>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 3402
+    - **Total Edges**: 7621
+    - **Root Nodes**: 22
+    - **Leaf Nodes**: 2147
+
+Knowledge coverage
+------------------
+    - Classes: 1163
+    - Individuals: 0
+    - Properties: 5
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 7
+    - **Minimum Depth**: 0
+    - **Average Depth**: 2.46
+    - **Depth Variance**: 2.32
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 75
+    - **Minimum Breadth**: 2
+    - **Average Breadth**: 36.25
+    - **Breadth Variance**: 666.69
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 0
+    - **Taxonomic Relations**: 2978
+    - **Non-taxonomic Relations**: 0
+    - **Average Terms per Type**: 0.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import FIX
+
+    # Initialize and load ontology
+    ontology = FIX()
+    ontology.load("path/to/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

massspectrometry/massspectrometry.rst

@@ -0,0 +1,68 @@
+Mass Spectrometry Ontology (MassSpectrometry)
+========================================================================================================================
+
+Overview
+--------
+A structured controlled vocabulary for the annotation of experiments concerned with proteomics mass spectrometry.
+
+:Domain: Chemistry
+:Category: Mass Spectrometry, Proteomics
+:Current Version: None
+:Last Updated: 12:02:2025
+:Creator: Andreas Bertsch
+:License: Creative Commons 4.0
+:Format: OWL
+:Download: `Mass Spectrometry Ontology (MassSpectrometry) Homepage <https://terminology.tib.eu/ts/ontologies/MS>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 17851
+    - **Total Edges**: 51814
+    - **Root Nodes**: 3786
+    - **Leaf Nodes**: 7959
+
+Knowledge coverage
+------------------
+    - Classes: 3636
+    - Individuals: 0
+    - Properties: 12
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 6
+    - **Minimum Depth**: 0
+    - **Average Depth**: 1.16
+    - **Depth Variance**: 0.58
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 7345
+    - **Minimum Breadth**: 2
+    - **Average Breadth**: 2534.57
+    - **Breadth Variance**: 9465403.67
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 0
+    - **Taxonomic Relations**: 16046
+    - **Non-taxonomic Relations**: 2
+    - **Average Terms per Type**: 0.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import MassSpectrometry
+
+    # Initialize and load ontology
+    ontology = MassSpectrometry()
+    ontology.load("path/to/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

mop/mop.rst

@@ -0,0 +1,69 @@
+Molecular Process Ontology (MOP)
+========================================================================================================================
+
+Overview
+--------
+MOP is the molecular process ontology. It contains the molecular processes that underlie
+the name reaction ontology RXNO, for example cyclization, methylation and demethylation.
+
+:Domain: Chemistry
+:Category: Chemistry, Molecular Biology
+:Current Version: 2022-05-11
+:Last Updated: 2022-05-11
+:Creator: None
+:License: Creative Commons 4.0
+:Format: OWL
+:Download: `Molecular Process Ontology (MOP) Homepage <https://terminology.tib.eu/ts/ontologies/MOP>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 15794
+    - **Total Edges**: 41157
+    - **Root Nodes**: 3693
+    - **Leaf Nodes**: 8182
+
+Knowledge coverage
+------------------
+    - Classes: 3717
+    - Individuals: 0
+    - Properties: 11
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 6
+    - **Minimum Depth**: 0
+    - **Average Depth**: 1.09
+    - **Depth Variance**: 0.63
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 7300
+    - **Minimum Breadth**: 3
+    - **Average Breadth**: 2253.14
+    - **Breadth Variance**: 7474153.55
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 0
+    - **Taxonomic Relations**: 4171
+    - **Non-taxonomic Relations**: 47
+    - **Average Terms per Type**: 0.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import MOP
+
+    # Initialize and load ontology
+    ontology = MOP()
+    ontology.load("path/to/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

nmrcv/nmrcv.rst

@@ -0,0 +1,82 @@
+Nuclear Magnetic Resonance Controlled Vocabulary (NMRCV)
+========================================================================================================================
+
+Overview
+--------
+This artefact is an MSI-approved controlled vocabulary primarily developed under COSMOS EU and PhenoMeNal EU governance.
+The nmrCV is supporting the nmrML XML format with standardized terms. nmrML is a vendor agnostic open access NMR raw data standard.
+Its primaly role is analogous to the mzCV for the PSI-approved mzML XML format. It uses BFO2.0 as its Top level.
+This CV was derived from two predecessors (The NMR CV from the David Wishart Group, developed by Joseph Cruz)
+and the MSI nmr CV developed by Daniel Schober at the EBI. This simple taxonomy of terms (no DL semantics used)
+serves the nuclear magnetic resonance markup language (nmrML) with meaningful descriptors to amend the nmrML xml file
+with CV terms. Metabolomics scientists are encouraged to use this CV to annotrate their raw and experimental context data,
+i.e. within nmrML. The approach to have an exchange syntax mixed of an xsd and CV stems from the PSI mzML effort.
+The reason to branch out from an xsd into a CV is, that in areas where the terminology is likely to change faster
+than the nmrML xsd could be updated and aligned, an externally and decentrallised maintained CV can accompensate
+for such dynamics in a more flexible way. A second reason for this set-up is that semantic validity of CV terms
+used in an nmrML XML instance (allowed CV terms, position/relation to each other, cardinality) can be validated
+by rule-based proprietary validators: By means of cardinality specifications and XPath expressions defined
+in an XML mapping file (an instances of the CvMappingRules.xsd ), one can define what ontology terms are allowed
+in a specific location of the data model.
+
+:Domain: Chemistry
+:Category: Chemistry
+:Current Version: 1.1.0
+:Last Updated: 2017-10-19
+:Creator: Daniel Schober
+:License: Creative Commons 4.0
+:Format: OWL
+:Download: `Nuclear Magnetic Resonance Controlled Vocabulary (NMRCV) Homepage <https://terminology.tib.eu/ts/ontologies/NMRCV>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 1596
+    - **Total Edges**: 3951
+    - **Root Nodes**: 184
+    - **Leaf Nodes**: 662
+
+Knowledge coverage
+------------------
+    - Classes: 757
+    - Individuals: 0
+    - Properties: 0
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 5
+    - **Minimum Depth**: 0
+    - **Average Depth**: 1.01
+    - **Depth Variance**: 0.72
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 273
+    - **Minimum Breadth**: 2
+    - **Average Breadth**: 103.83
+    - **Breadth Variance**: 10836.47
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 0
+    - **Taxonomic Relations**: 792
+    - **Non-taxonomic Relations**: 0
+    - **Average Terms per Type**: 0.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import NMRCV
+
+    # Initialize and load ontology
+    ontology = NMRCV()
+    ontology.load("path/to/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

ontokin/ontokin.rst

@@ -0,0 +1,68 @@
+Chemical Kinetics Ontology (OntoKin)
+========================================================================================================================
+
+Overview
+--------
+OntoKin is an ontology developed for representing chemical kinetic reaction mechanisms.
+
+:Domain: Chemistry
+:Category: Chemistry
+:Current Version: 1.0
+:Last Updated: 08 February 2022
+:Creator: IEEE
+:License: Creative Commons 4.0
+:Format: OWL
+:Download: `Chemical Kinetics Ontology (OntoKin) Homepage <https://www.ontologyportal.org/>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 407
+    - **Total Edges**: 1011
+    - **Root Nodes**: 122
+    - **Leaf Nodes**: 103
+
+Knowledge coverage
+------------------
+    - Classes: 83
+    - Individuals: 0
+    - Properties: 136
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 8
+    - **Minimum Depth**: 0
+    - **Average Depth**: 1.64
+    - **Depth Variance**: 2.39
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 122
+    - **Minimum Breadth**: 1
+    - **Average Breadth**: 45.22
+    - **Breadth Variance**: 1858.40
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 0
+    - **Taxonomic Relations**: 138
+    - **Non-taxonomic Relations**: 1
+    - **Average Terms per Type**: 0.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import OntoKin
+
+    # Initialize and load ontology
+    ontology = OntoKin()
+    ontology.load("path/to/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

proco/proco.rst

@@ -0,0 +1,69 @@
+PROcess Chemistry Ontology (PROCO)
+========================================================================================================================
+
+Overview
+--------
+PROCO (PROcess Chemistry Ontology) is a formal ontology that aims to standardly represent entities
+and relations among entities in the domain of process chemistry.
+
+:Domain: Chemistry
+:Category: Chemicals, Processes
+:Current Version: 04-14-2022
+:Last Updated: 04-14-2022
+:Creator: Anna Dun, Wes A. Schafer, Yongqun "Oliver" He (YH), Zachary Dance
+:License: Creative Commons 4.0
+:Format: OWL
+:Download: `PROcess Chemistry Ontology (PROCO) Homepage <https://github.com/proco-ontology/PROCO>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 6258
+    - **Total Edges**: 11796
+    - **Root Nodes**: 89
+    - **Leaf Nodes**: 4646
+
+Knowledge coverage
+------------------
+    - Classes: 970
+    - Individuals: 14
+    - Properties: 61
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 15
+    - **Minimum Depth**: 0
+    - **Average Depth**: 3.35
+    - **Depth Variance**: 8.54
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 228
+    - **Minimum Breadth**: 1
+    - **Average Breadth**: 60.19
+    - **Breadth Variance**: 4521.40
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 14
+    - **Taxonomic Relations**: 2975
+    - **Non-taxonomic Relations**: 1
+    - **Average Terms per Type**: 7.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import PROCO
+
+    # Initialize and load ontology
+    ontology = PROCO()
+    ontology.load("path/to/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

psimod/psimod.rst

@@ -0,0 +1,72 @@
+Proteomics Standards Initiative (PSI) Protein Modifications Ontology (PSI-MOD)
+========================================================================================================================
+
+Overview
+--------
+PSI-MOD is an ontology consisting of terms that describe protein chemical modifications,
+logically linked by an is_a relationship in such a way as to form a direct acyclic graph (DAG).
+The PSI-MOD ontology has more than 45 top-level nodes, and provides alternative hierarchical paths
+for classifying protein modifications either by the molecular structure of the modification,
+or by the amino acid residue that is modified.
+
+:Domain: Chemistry
+:Category: Protein Modifications
+:Current Version: 1.031.6
+:Last Updated: 2022-06-13
+:Creator: None
+:License: Creative Commons Attribution 4.0
+:Format: OWL
+:Download: `Proteomics Standards Initiative (PSI) Protein Modifications Ontology (PSI-MOD) Homepage <https://github.com/HUPO-PSI/psi-mod-CV>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 28523
+    - **Total Edges**: 86380
+    - **Root Nodes**: 9338
+    - **Leaf Nodes**: 16902
+
+Knowledge coverage
+------------------
+    - Classes: 2098
+    - Individuals: 0
+    - Properties: 4
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 4
+    - **Minimum Depth**: 0
+    - **Average Depth**: 0.95
+    - **Depth Variance**: 0.60
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 11284
+    - **Minimum Breadth**: 4
+    - **Average Breadth**: 5684.00
+    - **Breadth Variance**: 22690827.20
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 0
+    - **Taxonomic Relations**: 8347
+    - **Non-taxonomic Relations**: 0
+    - **Average Terms per Type**: 0.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import PSIMOD
+
+    # Initialize and load ontology
+    ontology = PSIMOD()
+    ontology.load("path/to/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

rex/rex.rst

@@ -0,0 +1,70 @@
+Physico-chemical process ontology (REX)
+========================================================================================================================
+
+Overview
+--------
+REX is an ontology of physico-chemical processes, i.e. physico-chemical changes occurring in course of time.
+REX includes both microscopic processes (involving molecular entities or subatomic particles) and macroscopic processes.
+Some biochemical processes from Gene Ontology (GO Biological process) can be described as instances of REX.
+
+:Domain: Chemistry
+:Category: Chemistry
+:Current Version: 1.0
+:Last Updated: 2025-03-11
+:Creator: University of Warsaw
+:License: Creative Commons 4.0
+:Format: OWL, RDF
+:Download: `Physico-chemical process ontology (REX) Homepage <https://terminology.tib.eu/ts/ontologies/REX>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 2461
+    - **Total Edges**: 5630
+    - **Root Nodes**: 356
+    - **Leaf Nodes**: 1457
+
+Knowledge coverage
+------------------
+    - Classes: 552
+    - Individuals: 0
+    - Properties: 6
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 6
+    - **Minimum Depth**: 0
+    - **Average Depth**: 1.35
+    - **Depth Variance**: 0.97
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 978
+    - **Minimum Breadth**: 5
+    - **Average Breadth**: 304.57
+    - **Breadth Variance**: 116930.53
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 0
+    - **Taxonomic Relations**: 1126
+    - **Non-taxonomic Relations**: 0
+    - **Average Terms per Type**: 0.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import REX
+
+    # Initialize and load ontology
+    ontology = REX()
+    ontology.load("path/to/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

rxno/rxno.rst

@@ -0,0 +1,69 @@
+Reaction Ontology (RXNO)
+========================================================================================================================
+
+Overview
+--------
+RXNO is the name reaction ontology. It contains more than 500 classes representing organic reactions
+such as the Diels–Alder cyclization.
+
+:Domain: Chemistry
+:Category: Chemistry
+:Current Version: None
+:Last Updated: 2021-12-16
+:Creator: None
+:License: Creative Commons 4.0
+:Format: OWL
+:Download: `Reaction Ontology (RXNO) Homepage <https://github.com/rsc-ontologies/rxno>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 5676
+    - **Total Edges**: 14841
+    - **Root Nodes**: 845
+    - **Leaf Nodes**: 2924
+
+Knowledge coverage
+------------------
+    - Classes: 1109
+    - Individuals: 0
+    - Properties: 14
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 8
+    - **Minimum Depth**: 0
+    - **Average Depth**: 1.71
+    - **Depth Variance**: 1.67
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 2230
+    - **Minimum Breadth**: 12
+    - **Average Breadth**: 623.00
+    - **Breadth Variance**: 588146.89
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 0
+    - **Taxonomic Relations**: 3757
+    - **Non-taxonomic Relations**: 16
+    - **Average Terms per Type**: 0.00
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import RXNO
+
+    # Initialize and load ontology
+    ontology = RXNO()
+    ontology.load("path/to/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

vibso/vibso.rst

@@ -0,0 +1,69 @@
+Vibrational Spectroscopy Ontology (VIBSO)
+========================================================================================================================
+
+Overview
+--------
+The Vibration Spectroscopy Ontology defines technical terms with which research data produced
+in vibrational spectroscopy experiments can be semantically enriched, made machine readable and FAIR.
+
+:Domain: Chemistry
+:Category: Spectroscopy
+:Current Version: 2024-09-23
+:Last Updated: 2024-09-23
+:Creator: VIBSO Workgroup
+:License: Creative Commons Attribution 4.0
+:Format: OWL
+:Download: `Vibrational Spectroscopy Ontology (VIBSO) Homepage <https://terminology.tib.eu/ts/ontologies/vibso>`_
+
+Graph Metrics
+-------------
+    - **Total Nodes**: 4007
+    - **Total Edges**: 8009
+    - **Root Nodes**: 328
+    - **Leaf Nodes**: 2547
+
+Knowledge coverage
+------------------
+    - Classes: 598
+    - Individuals: 40
+    - Properties: 53
+
+Hierarchical metrics
+--------------------
+    - **Maximum Depth**: 13
+    - **Minimum Depth**: 0
+    - **Average Depth**: 2.03
+    - **Depth Variance**: 2.77
+
+Breadth metrics
+------------------
+    - **Maximum Breadth**: 1131
+    - **Minimum Breadth**: 1
+    - **Average Breadth**: 188.29
+    - **Breadth Variance**: 98075.49
+
+Dataset Statistics
+------------------
+Generated Benchmarks:
+    - **Term Types**: 40
+    - **Taxonomic Relations**: 856
+    - **Non-taxonomic Relations**: 23
+    - **Average Terms per Type**: 2.35
+
+Usage Example
+-------------
+.. code-block:: python
+
+    from ontolearner.ontology import VIBSO
+
+    # Initialize and load ontology
+    ontology = VIBSO()
+    ontology.load("path/to/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