hamedbabaeigiglou commited on
Commit
72ee457
·
1 Parent(s): 6fb711c

:sparkles: Added MDSOnto ontology!

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README.md CHANGED
@@ -5,78 +5,81 @@ language:
5
  - en
6
  tags:
7
  - OntoLearner
8
- - ontology-learning
9
- - materials-science-and-engineering
 
10
  pretty_name: Materials Science And Engineering
11
  ---
 
12
  <div align="center">
13
- <img src="https://raw.githubusercontent.com/sciknoworg/OntoLearner/main/images/logo.png" alt="OntoLearner"
14
- style="display: block; margin: 0 auto; width: 500px; height: auto;">
15
- <h1 style="text-align: center; margin-top: 1em;">Materials Science And Engineering Domain Ontologies</h1>
16
- <a href="https://github.com/sciknoworg/OntoLearner"><img src="https://img.shields.io/badge/GitHub-OntoLearner-blue?logo=github" /></a>
17
  </div>
18
 
19
-
20
  ## Overview
21
  Materials Science and Engineering is a multidisciplinary domain that focuses on the study and application of materials, emphasizing their structure, properties, processing, and performance in engineering contexts. This field is pivotal for advancing knowledge representation, as it integrates principles from physics, chemistry, and engineering to innovate and optimize materials for diverse technological applications. By systematically categorizing and modeling material-related data, this domain facilitates the development of new materials and enhances the understanding of their behavior under various conditions.
22
 
23
- ## Ontologies
24
  | Ontology ID | Full Name | Classes | Properties | Last Updated |
25
- |-------------|-----------|---------|------------|--------------|
26
- | AMOntology | Additive Manufacturing Ontology (AMOntology) | 328 | 21 | 2023-05-10|
27
- | ASMO | Atomistic Simulation Methods Ontology (ASMO) | 99 | 41 | None|
28
- | Atomistic | Atomistic Ontology (Atomistic) | 12 | 2 | None|
29
- | BattINFO | Battery Interface Ontology (BattINFO) | 4431 | 304 | None|
30
- | BMO | Building Material Ontology (BMO) | 24 | 62 | 2019-12-10|
31
- | BVCO | Battery Value Chain Ontology (BVCO) | 262 | 6 | None|
32
- | CDCO | Crystallographic Defect Core Ontology (CDCO) | 7 | 2 | None|
33
- | CHAMEO | Characterisation Methodology Domain Ontology (CHAMEO) | 203 | 52 | 2024-04-12|
34
- | CIFCore | Crystallographic Information Framework Core Dictionary (CIFCore) | 1182 | 0 | May 24, 2023|
35
- | CMSO | Computational Material Sample Ontology (CMSO) | 45 | 51 | None|
36
- | DISO | Dislocation Ontology (DISO) | 62 | 45 | 21.03.202|
37
- | DSIM | Dislocation Simulation and Model Ontology (DSIM) | 47 | 78 | 17.08.2023|
38
  | EMMO | The Elementary Multiperspective Material Ontology (EMMO) | 2448 | 181 | 2024-03|
39
- | EMMOCrystallography | Crystallography Ontology (EMMOCrystallography) | 61 | 5 | None|
40
- | FSO | Flow Systems Ontology (FSO) | 14 | 22 | 2020-08-06|
41
- | GPO | General Process Ontology (GPO) | 187 | 17 | None|
42
- | HPOnt | The Heat Pump Ontology (HPOnt) | 4 | 12 | None|
43
- | LDO | Line Defect Ontology (LDO) | 30 | 11 | None|
44
  | LPBFO | Laser Powder Bed Fusion Ontology (LPBFO) | 508 | 38 | 2022-09-20|
45
- | MAMBO | Molecules And Materials Basic Ontology (MAMBO) | 57 | 104 | None|
46
- | MAT | Material Properties Ontology (MAT) | 140 | 21 | None|
47
- | MaterialInformation | Material Information Ontology (MaterialInformation) | 548 | 98 | None|
48
- | MatOnto | Material Ontology (MatOnto) | 1307 | 95 | None|
49
- | MatVoc | Materials Vocabulary (MatVoc) | 28 | 15 | 2022-12-12|
50
- | MatWerk | NFDI MatWerk Ontology (MatWerk) | 449 | 129 | 2025-03-01|
51
- | MDO | Materials Design Ontology (MDO) | 13 | 13 | 2022-08-02|
52
- | MDS | Materials Data Science Ontology (MDS) | 363 | 10 | 03/24/2024|
53
- | MechanicalTesting | Mechanical Testing Ontology (MechanicalTesting) | 369 | 5 | None|
54
- | MicroStructures | EMMO-based ontology for microstructures (MicroStructures) | 43 | 0 | None|
55
- | MMO | Materials Mechanics Ontology (MMO) | 428 | 17 | 2024-01-30|
56
  | MOLBRINELL | MatoLab Brinell Test Ontology (MOL_BRINELL) | 37 | 21 | 05/05/2022|
 
 
 
 
57
  | MOLTENSILE | Matolab Tensile Test Ontology (MOL_TENSILE) | 371 | 95 | 04/16/2021|
58
- | MSEO | Materials Science and Engineering Ontology (MSEO) | 138 | 2 | None|
 
 
 
 
 
 
 
 
 
 
 
 
 
59
  | MSLE | Material Science Lab Equipment Ontology (MSLE) | 45 | 10 | Sep 15, 2022|
60
- | NanoMine | NanoMine Ontology (NanoMine) | 157 | 0 | None|
61
- | OIEManufacturing | Open Innovation Environment Manufacturing (OIEManufacturing) | 222 | 3 | None|
62
- | OIEMaterials | Open Innovation Environment Materials (OIEMaterials) | 119 | 0 | None|
63
- | OIEModels | Open Innovation Environment Models (OIEModels) | 108 | 1 | None|
64
- | OIESoftware | Open Innovation Environment Software (OIESoftware) | 155 | 0 | None|
65
  | ONTORULE | Ontology for the Steel Domain (ONTORULE) | 24 | 37 | 2010-05-31|
66
- | PeriodicTable | Periodic Table of the Elements Ontology (PeriodicTable) | 6 | 13 | 2004/02/05|
67
- | Photovoltaics | EMMO Domain Ontology for Photovoltaics (Photovoltaics) | 47 | 3 | None|
68
- | PLDO | Planar Defects Ontology (PLDO) | 27 | 15 | None|
69
- | PMDco | The Platform MaterialDigital core ontology (PMDco) | 1002 | 66 | 2025-03-20|
70
- | PODO | Point Defects Ontology (PODO) | 12 | 5 | None|
71
- | PRIMA | PRovenance Information in MAterials science (PRIMA) | 67 | 67 | 2024-01-29|
72
- | SSN | Semantic Sensor Network Ontology (SSN) | 22 | 38 | 2017-04-17|
73
  | SystemCapabilities | System Capabilities Ontology (SystemCapabilities) | 25 | 8 | 2017-05-14|
74
- | VIMMP | Virtual Materials Marketplace Ontologies (VIMMP) | 1234 | 771 | 2021-01-02|
 
 
 
 
 
 
 
 
 
 
 
75
 
76
  ## Dataset Files
77
  Each ontology directory contains the following files:
78
  1. `<ontology_id>.<format>` - The original ontology file
79
- 2. `term_typings.json` - A Dataset of term-to-type mappings
80
  3. `taxonomies.json` - Dataset of taxonomic relations
81
  4. `non_taxonomic_relations.json` - Dataset of non-taxonomic relations
82
  5. `<ontology_id>.rst` - Documentation describing the ontology
@@ -92,30 +95,29 @@ pip install ontolearner
92
 
93
  **How to load an ontology or LLM4OL Paradigm tasks datasets?**
94
  ``` python
95
- from ontolearner import AMOntology
96
 
97
- ontology = AMOntology()
98
 
99
  # Load an ontology.
100
- ontology.load()
101
 
102
  # Load (or extract) LLMs4OL Paradigm tasks datasets
103
  data = ontology.extract()
104
  ```
105
 
106
-
107
  **How use the loaded dataset for LLM4OL Paradigm task settings?**
108
  ``` python
109
  # Import core modules from the OntoLearner library
110
- from ontolearner import AMOntology, LearnerPipeline, train_test_split
111
 
112
- # Load the AMOntology ontology, which contains concepts related to wines, their properties, and categories
113
- ontology = AMOntology()
114
  ontology.load() # Load entities, types, and structured term annotations from the ontology
115
- data = ontology.extract()
116
 
117
- # Split into train and test sets
118
- train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
119
 
120
  # Initialize a multi-component learning pipeline (retriever + LLM)
121
  # This configuration enables a Retrieval-Augmented Generation (RAG) setup
@@ -148,16 +150,14 @@ print(outputs)
148
 
149
  For more detailed documentation, see the [![Documentation](https://img.shields.io/badge/Documentation-ontolearner.readthedocs.io-blue)](https://ontolearner.readthedocs.io)
150
 
151
-
152
  ## Citation
153
 
154
  If you find our work helpful, feel free to give us a cite.
155
 
156
-
157
  ```bibtex
158
  @inproceedings{babaei2023llms4ol,
159
  title={LLMs4OL: Large language models for ontology learning},
160
- author={Babaei Giglou, Hamed and D’Souza, Jennifer and Auer, S{\"o}ren},
161
  booktitle={International Semantic Web Conference},
162
  pages={408--427},
163
  year={2023},
 
5
  - en
6
  tags:
7
  - OntoLearner
8
+ - Ontology-Learning
9
+ - Materials-Science-And-Engineering
10
+ - Benchmark-Ontologies
11
  pretty_name: Materials Science And Engineering
12
  ---
13
+
14
  <div align="center">
15
+ <img src="https://raw.githubusercontent.com/sciknoworg/OntoLearner/main/images/logo.png" alt="OntoLearner"
16
+ style="display: block; margin: 0 auto; width: 500px; height: auto;">
17
+ <h1 style="text-align: center; margin-top: 1em;">Materials Science And Engineering Domain Ontologies</h1>
18
+ <a href="https://github.com/sciknoworg/OntoLearner"><img src="https://img.shields.io/badge/GitHub-OntoLearner-blue?logo=github" /></a>
19
  </div>
20
 
 
21
  ## Overview
22
  Materials Science and Engineering is a multidisciplinary domain that focuses on the study and application of materials, emphasizing their structure, properties, processing, and performance in engineering contexts. This field is pivotal for advancing knowledge representation, as it integrates principles from physics, chemistry, and engineering to innovate and optimize materials for diverse technological applications. By systematically categorizing and modeling material-related data, this domain facilitates the development of new materials and enhances the understanding of their behavior under various conditions.
23
 
24
+
25
  | Ontology ID | Full Name | Classes | Properties | Last Updated |
26
+ |-------------|-----------|---------|------------|-------------|
27
+ | BattINFO | Battery Interface Ontology (BattINFO) | 4431 | 304 | nan|
28
+ | SSN | Semantic Sensor Network Ontology (SSN) | 22 | 38 | 2017-04-17|
 
 
 
 
 
 
 
 
 
 
29
  | EMMO | The Elementary Multiperspective Material Ontology (EMMO) | 2448 | 181 | 2024-03|
30
+ | PRIMA | PRovenance Information in MAterials science (PRIMA) | 67 | 67 | 2024-01-29|
31
+ | CIFCore | Crystallographic Information Framework Core Dictionary (CIFCore) | 1182 | 0 | May 24, 2023|
 
 
 
32
  | LPBFO | Laser Powder Bed Fusion Ontology (LPBFO) | 508 | 38 | 2022-09-20|
33
+ | MDSOnto | The Modular Ontology for Materials and Data Science (MDS-Onto) | 1710 | 169 | 2026-02-03|
34
+ | VIMMP | Virtual Materials Marketplace Ontologies (VIMMP) | 1234 | 771 | 2021-01-02|
 
 
 
 
 
 
 
 
 
35
  | MOLBRINELL | MatoLab Brinell Test Ontology (MOL_BRINELL) | 37 | 21 | 05/05/2022|
36
+ | MatOnto | Material Ontology (MatOnto) | 1307 | 95 | nan|
37
+ | PMDco | The Platform MaterialDigital core ontology (PMDco) | 1002 | 66 | 2025-03-20|
38
+ | MatWerk | NFDI MatWerk Ontology (MatWerk) | 449 | 129 | 2025-03-01|
39
+ | MaterialInformation | Material Information Ontology (MaterialInformation) | 548 | 98 | nan|
40
  | MOLTENSILE | Matolab Tensile Test Ontology (MOL_TENSILE) | 371 | 95 | 04/16/2021|
41
+ | AMOntology | Additive Manufacturing Ontology (AMOntology) | 328 | 21 | 2023-05-10|
42
+ | MMO | Materials Mechanics Ontology (MMO) | 428 | 17 | 2024-01-30|
43
+ | MechanicalTesting | Mechanical Testing Ontology (MechanicalTesting) | 369 | 5 | nan|
44
+ | CHAMEO | Characterisation Methodology Domain Ontology (CHAMEO) | 203 | 52 | 2024-04-12|
45
+ | MDS | Materials Data Science Ontology (MDS) | 363 | 10 | 03/24/2024|
46
+ | PeriodicTable | Periodic Table of the Elements Ontology (PeriodicTable) | 6 | 13 | 2004/02/05|
47
+ | BVCO | Battery Value Chain Ontology (BVCO) | 262 | 6 | nan|
48
+ | ASMO | Atomistic Simulation Methods Ontology (ASMO) | 99 | 41 | nan|
49
+ | NanoMine | NanoMine Ontology (NanoMine) | 157 | 0 | nan|
50
+ | OIEManufacturing | Open Innovation Environment Manufacturing (OIEManufacturing) | 222 | 3 | nan|
51
+ | MSEO | Materials Science and Engineering Ontology (MSEO) | 138 | 2 | nan|
52
+ | MAT | Material Properties Ontology (MAT) | 140 | 21 | nan|
53
+ | GPO | General Process Ontology (GPO) | 187 | 17 | nan|
54
+ | DISO | Dislocation Ontology (DISO) | 62 | 45 | 21.03.202|
55
  | MSLE | Material Science Lab Equipment Ontology (MSLE) | 45 | 10 | Sep 15, 2022|
56
+ | DSIM | Dislocation Simulation and Model Ontology (DSIM) | 47 | 78 | 17.08.2023|
 
 
 
 
57
  | ONTORULE | Ontology for the Steel Domain (ONTORULE) | 24 | 37 | 2010-05-31|
58
+ | OIEMaterials | Open Innovation Environment Materials (OIEMaterials) | 119 | 0 | nan|
59
+ | OIESoftware | Open Innovation Environment Software (OIESoftware) | 155 | 0 | nan|
60
+ | MAMBO | Molecules And Materials Basic Ontology (MAMBO) | 57 | 104 | nan|
61
+ | CMSO | Computational Material Sample Ontology (CMSO) | 45 | 51 | nan|
62
+ | EMMOCrystallography | Crystallography Ontology (EMMOCrystallography) | 61 | 5 | nan|
63
+ | MicroStructures | EMMO-based ontology for microstructures (MicroStructures) | 43 | 0 | nan|
64
+ | OIEModels | Open Innovation Environment Models (OIEModels) | 108 | 1 | nan|
65
  | SystemCapabilities | System Capabilities Ontology (SystemCapabilities) | 25 | 8 | 2017-05-14|
66
+ | PLDO | Planar Defects Ontology (PLDO) | 27 | 15 | nan|
67
+ | Photovoltaics | EMMO Domain Ontology for Photovoltaics (Photovoltaics) | 47 | 3 | nan|
68
+ | BMO | Building Material Ontology (BMO) | 24 | 62 | 2019-12-10|
69
+ | FSO | Flow Systems Ontology (FSO) | 14 | 22 | 2020-08-06|
70
+ | PODO | Point Defects Ontology (PODO) | 12 | 5 | nan|
71
+ | LDO | Line Defect Ontology (LDO) | 30 | 11 | nan|
72
+ | MatVoc | Materials Vocabulary (MatVoc) | 28 | 15 | 2022-12-12|
73
+ | OIECharacterisation | Open Innovation Environment Characterisation (OIECharacterisation) | 42 | 0 | nan|
74
+ | HPOnt | The Heat Pump Ontology (HPOnt) | 4 | 12 | nan|
75
+ | MDO | Materials Design Ontology (MDO) | 13 | 13 | 2022-08-02|
76
+ | CDCO | Crystallographic Defect Core Ontology (CDCO) | 7 | 2 | nan|
77
+ | Atomistic | Atomistic Ontology (Atomistic) | 12 | 2 | nan|
78
 
79
  ## Dataset Files
80
  Each ontology directory contains the following files:
81
  1. `<ontology_id>.<format>` - The original ontology file
82
+ 2. `term_typings.json` - Dataset of term to type mappings
83
  3. `taxonomies.json` - Dataset of taxonomic relations
84
  4. `non_taxonomic_relations.json` - Dataset of non-taxonomic relations
85
  5. `<ontology_id>.rst` - Documentation describing the ontology
 
95
 
96
  **How to load an ontology or LLM4OL Paradigm tasks datasets?**
97
  ``` python
98
+ from ontolearner import BattINFO
99
 
100
+ ontology = BattINFO()
101
 
102
  # Load an ontology.
103
+ ontology.load()
104
 
105
  # Load (or extract) LLMs4OL Paradigm tasks datasets
106
  data = ontology.extract()
107
  ```
108
 
 
109
  **How use the loaded dataset for LLM4OL Paradigm task settings?**
110
  ``` python
111
  # Import core modules from the OntoLearner library
112
+ from ontolearner import BattINFO, LearnerPipeline, train_test_split
113
 
114
+ # Load the BattINFO ontology, which contains concepts related to wines, their properties, and categories
115
+ ontology = BattINFO()
116
  ontology.load() # Load entities, types, and structured term annotations from the ontology
117
+ ontological_data = ontology.extract()
118
 
119
+ # Split instances into train and test sets
120
+ train_data, test_data = train_test_split(ontological_data, test_size=0.2, random_state=42)
121
 
122
  # Initialize a multi-component learning pipeline (retriever + LLM)
123
  # This configuration enables a Retrieval-Augmented Generation (RAG) setup
 
150
 
151
  For more detailed documentation, see the [![Documentation](https://img.shields.io/badge/Documentation-ontolearner.readthedocs.io-blue)](https://ontolearner.readthedocs.io)
152
 
 
153
  ## Citation
154
 
155
  If you find our work helpful, feel free to give us a cite.
156
 
 
157
  ```bibtex
158
  @inproceedings{babaei2023llms4ol,
159
  title={LLMs4OL: Large language models for ontology learning},
160
+ author={Babaei Giglou, Hamed and D’Souza, Jennifer and Auer, S{"o}ren},
161
  booktitle={International Semantic Web Conference},
162
  pages={408--427},
163
  year={2023},
mdsonto/mdsonto.owl ADDED
The diff for this file is too large to render. See raw diff
 
mdsonto/mdsonto.rst ADDED
@@ -0,0 +1,134 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ The Modular Ontology for Materials and Data Science (MDS-Onto)
2
+ ==============================================================================
3
+
4
+ .. sidebar::
5
+
6
+ .. list-table:: **Ontology Card**
7
+ :header-rows: 0
8
+
9
+ * - **Domain**
10
+ - Materials Science and Engineering
11
+ * - **Category**
12
+ - Materials Data Science
13
+ * - **Current Version**
14
+ - 0.3.1.16
15
+ * - **Last Updated**
16
+ - 2026-02-03
17
+ * - **Creator**
18
+ - SDLE Research Center
19
+ * - **License**
20
+ - CC BY-SA 4.0
21
+ * - **Format**
22
+ - owl
23
+ * - **Download**
24
+ - `Download The Modular Ontology for Materials and Data Science (MDS-Onto) <https://cwrusdle.bitbucket.io/files/MDS_Onto/index-en.html>`_
25
+
26
+ MDS-Onto is a domain (low) level ontology that describes terms in Materials Data Science. It is divided into six large modules: BuiltEnv, Exposure, Chemistry, Manufacture, Characterization, and Geospatial. Under each module, there are multiple sub-modules such as FTIR, AFM, Chem-Rxn, PV-Module, Accelerated Exposure, etc.
27
+
28
+ Metrics & Statistics
29
+ --------------------------
30
+
31
+ .. tab:: Graph
32
+
33
+
34
+ .. list-table:: Graph Statistics
35
+ :widths: 50 50
36
+ :header-rows: 0
37
+
38
+ * - **Total Nodes**
39
+ - 4362
40
+ * - **Total Edges**
41
+ - 13095
42
+ * - **Root Nodes**
43
+ - 92
44
+ * - **Leaf Nodes**
45
+ - 2226
46
+ ::
47
+
48
+
49
+ .. tab:: Coverage
50
+
51
+
52
+ .. list-table:: Knowledge Coverage Statistics
53
+ :widths: 50 50
54
+ :header-rows: 0
55
+
56
+ * - **Classes**
57
+ - 1710
58
+ * - **Individuals**
59
+ - 1
60
+ * - **Properties**
61
+ - 169
62
+
63
+ ::
64
+
65
+ .. tab:: Hierarchy
66
+
67
+
68
+ .. list-table:: Hierarchical Metrics
69
+ :widths: 50 50
70
+ :header-rows: 0
71
+
72
+ * - **Maximum Depth**
73
+ - 7
74
+ * - **Minimum Depth**
75
+ - 0
76
+ * - **Average Depth**
77
+ - 1.03
78
+ * - **Depth Variance**
79
+ - 2.01
80
+ ::
81
+
82
+
83
+ .. tab:: Breadth
84
+
85
+
86
+ .. list-table:: Breadth Metrics
87
+ :widths: 50 50
88
+ :header-rows: 0
89
+
90
+ * - **Maximum Breadth**
91
+ - 92
92
+ * - **Minimum Breadth**
93
+ - 1
94
+ * - **Average Breadth**
95
+ - 25.75
96
+ * - **Breadth Variance**
97
+ - 1093.94
98
+ ::
99
+
100
+ .. tab:: LLMs4OL
101
+
102
+
103
+ .. list-table:: LLMs4OL Dataset Statistics
104
+ :widths: 50 50
105
+ :header-rows: 0
106
+
107
+ * - **Term Types**
108
+ - 1
109
+ * - **Taxonomic Relations**
110
+ - 1648
111
+ * - **Non-taxonomic Relations**
112
+ - 259
113
+ * - **Average Terms per Type**
114
+ - 1.00
115
+ ::
116
+
117
+ Usage Example
118
+ -------------
119
+ Use the following code to import this ontology programmatically:
120
+
121
+ .. code-block:: python
122
+
123
+ from ontolearner.ontology import MDSOnto
124
+
125
+ ontology = MDSOnto()
126
+ ontology.load("path/to/MDSOnto-ontology.owl")
127
+
128
+ # Extract datasets
129
+ data = ontology.extract()
130
+
131
+ # Access specific relations
132
+ term_types = data.term_typings
133
+ taxonomic_relations = data.type_taxonomies
134
+ non_taxonomic_relations = data.type_non_taxonomic_relations
mdsonto/term_typings.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "ID": "TT_3b0d4b8e",
4
+ "term": "Directly precedes",
5
+ "types": [
6
+ "IrreflectiveProperty"
7
+ ]
8
+ }
9
+ ]
mdsonto/type_non_taxonomic_relations.json ADDED
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1
+ {
2
+ "types": [
3
+ "AFMInstrumentModel",
4
+ "Act of Artifact Processing",
5
+ "Act of Data Transformation",
6
+ "ActOfCharacterizing",
7
+ "ActOfPowderBedFusion",
8
+ "ActOfPowderBedFusionLaserBeamMetal",
9
+ "Ad2",
10
+ "Agent",
11
+ "Algorithm",
12
+ "Alloy",
13
+ "AmorphousMetal",
14
+ "AmorphousPhase",
15
+ "Amount",
16
+ "AnalyticalResult",
17
+ "Artifact Identifier",
18
+ "BackscatterFoil",
19
+ "Backsheet",
20
+ "BacksheetCoreLayer",
21
+ "BacksheetInnerLayer",
22
+ "BacksheetOuterLayer",
23
+ "BatchNumber",
24
+ "BoardType",
25
+ "BuildJobName",
26
+ "CantileverForceConstant",
27
+ "CantileverMaterial",
28
+ "CantileverResonanceFrequency",
29
+ "CaptureRateMax",
30
+ "CaptureRateMin",
31
+ "Cell",
32
+ "CellCutType",
33
+ "CellTechnologyType",
34
+ "CeramicMaterial",
35
+ "CeramicMatrixComposite",
36
+ "Cleanliness",
37
+ "Code Identifier",
38
+ "CompositeMaterial",
39
+ "CompressionStage",
40
+ "CompressionStageTiltX",
41
+ "CompressionStageTiltXInfo",
42
+ "CompressionStageTiltXOffset",
43
+ "CompressionStageTiltXOffsetInfo",
44
+ "CompressionStageTiltZ",
45
+ "CompressionStageTiltZInfo",
46
+ "CompressionStageTiltZOffset",
47
+ "CompressionStageTiltZOffsetInfo",
48
+ "CompressionStageX",
49
+ "CompressionStageXInfo",
50
+ "CompressionStageY",
51
+ "CompressionStageYInfo",
52
+ "CompressionStageZ",
53
+ "CompressionStageZInfo",
54
+ "Computer",
55
+ "Computing Artifact Function",
56
+ "Continent",
57
+ "CrystallineAlloy",
58
+ "CrystallineCeramicMaterial",
59
+ "CrystallinePhase",
60
+ "CurrentVoltageRecipe",
61
+ "Data Range Interval Estimate Information Content Entity",
62
+ "Date Identifier",
63
+ "Depth",
64
+ "Descriptive Information Content Entity",
65
+ "Designative Information Content Entity",
66
+ "Designative Name",
67
+ "DetectionWavelength",
68
+ "DetectorType",
69
+ "Directive Information Content Entity",
70
+ "DistanceFromSample",
71
+ "Encapsulant",
72
+ "EnergyUsed",
73
+ "ExposureTime",
74
+ "Facility",
75
+ "FileExtension",
76
+ "FilterType",
77
+ "FingerAmount",
78
+ "FocalDistance",
79
+ "FoilInfo",
80
+ "FrameRate",
81
+ "Frequency",
82
+ "Gap",
83
+ "Geometry",
84
+ "Geospatial Coordinate Reference System",
85
+ "Geospatial Polygon",
86
+ "Geospatial Region",
87
+ "Geospatial Region Bounding Box",
88
+ "Glass",
89
+ "HatchDistance",
90
+ "Height",
91
+ "HotStage",
92
+ "HotStageDiameter",
93
+ "HotStageTemperature",
94
+ "Image",
95
+ "Information Bearing Artifact",
96
+ "Information Bearing Entity",
97
+ "Information Content Entity",
98
+ "InstrumentID",
99
+ "InstrumentName",
100
+ "IntegralGain",
101
+ "Intensity",
102
+ "InterFeatureCoolingTime",
103
+ "InterSampleGap",
104
+ "InterdigitatedCombIdentifier",
105
+ "LPBFO_00746",
106
+ "LPBFO_00809",
107
+ "LaserPower",
108
+ "Length",
109
+ "LineDefect",
110
+ "Magnification",
111
+ "ManufacturingMethod",
112
+ "Masking",
113
+ "Mass Density",
114
+ "Material Artifact",
115
+ "MaterialComposition",
116
+ "MaterialName",
117
+ "Maximum Ordinal Measurement Information Content Entity",
118
+ "Measurement Information Content Entity",
119
+ "Measurement Unit",
120
+ "Mechanical Process",
121
+ "MetadataFormat",
122
+ "MetalMatrixComposite",
123
+ "MetallicMaterial",
124
+ "Non-Name Identifier",
125
+ "Objective",
126
+ "One Dimensional Extent",
127
+ "Organization",
128
+ "PhotovoltaicModule",
129
+ "PiezoelectricSensor",
130
+ "PiezoelectricSensorLocation",
131
+ "PilatusAmbientTemp",
132
+ "PilatusAmbientTempInfo",
133
+ "PilatusDewPoint",
134
+ "PilatusDewPointInfo",
135
+ "PilatusExposeTime",
136
+ "PilatusExposeTimeInfo",
137
+ "PilatusFrameIndex",
138
+ "PilatusFrameIndexInfo",
139
+ "PilatusFrameTotal",
140
+ "PilatusFrameTotalInfo",
141
+ "PilatusHumidity",
142
+ "PilatusHumidityInfo",
143
+ "PilatusReadTemp",
144
+ "PilatusReadTempInfo",
145
+ "PilatusSetTemp",
146
+ "PilatusSetTempInfo",
147
+ "PlanarDefect",
148
+ "PlateNumber",
149
+ "Plating",
150
+ "PointDefect",
151
+ "PointDistance",
152
+ "PolymerMatrixComposite",
153
+ "PolymericMaterial",
154
+ "Position",
155
+ "PreFiltration",
156
+ "PreparedSurface",
157
+ "PrimaryPowder",
158
+ "ProcessingMethod",
159
+ "ProfilometryInstrument",
160
+ "ProfilometryModel",
161
+ "PureMetal",
162
+ "Ratio Measurement Information Content Entity",
163
+ "Recipe",
164
+ "Recording Device",
165
+ "Resolution",
166
+ "ResonanceFrequency",
167
+ "RunEndDate",
168
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169
+ "SAXSFrameNumber",
170
+ "SAXSFrameNumberInfo",
171
+ "SAXSPeltierPower",
172
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173
+ "Sample",
174
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175
+ "SampleHolderID",
176
+ "SampleHolderOrientation",
177
+ "SampleID",
178
+ "SampleIndexOnSampleHolder",
179
+ "SampleRate",
180
+ "SampleSize",
181
+ "Scale",
182
+ "ScanAngle",
183
+ "ScanLines",
184
+ "ScanParameters",
185
+ "ScanRate",
186
+ "ScanSpeed",
187
+ "SecondaryPowder",
188
+ "SensorPosition",
189
+ "SensorType",
190
+ "Sequence Position Ordinality",
191
+ "SerialNumber",
192
+ "Size",
193
+ "SliceLayerInterfaceFile",
194
+ "SliceLinesPerLayerFile",
195
+ "Software",
196
+ "SolventType",
197
+ "Spatial Reference System",
198
+ "SpincoaterInstrument",
199
+ "SpincoatingParameters",
200
+ "SpotSize",
201
+ "StorageConditions",
202
+ "StorageRingCurrent",
203
+ "StorageRingCurrentInfo",
204
+ "StorageRingHAngle",
205
+ "StorageRingHAngleInfo",
206
+ "StorageRingHPos",
207
+ "StorageRingHPosInfo",
208
+ "StorageRingVPos",
209
+ "StorageRingVPosInfo",
210
+ "Substrate",
211
+ "SubstrateDimensions",
212
+ "SubstrateMaterial",
213
+ "SubstrateType",
214
+ "TTLTrigger",
215
+ "TTLTriggerInfo",
216
+ "Thermocouple",
217
+ "ThermocoupleInfo",
218
+ "Thermoplastics",
219
+ "Thermosets",
220
+ "Thickness",
221
+ "TipMaterial",
222
+ "TomographyFrameNumber",
223
+ "TomographyFrameNumberInfo",
224
+ "Tool",
225
+ "ToolID",
226
+ "TraceMaterial",
227
+ "Turn",
228
+ "Undulator",
229
+ "UndulatorCurrent",
230
+ "UndulatorCurrentInfo",
231
+ "UndulatorGap",
232
+ "UndulatorGapInfo",
233
+ "VolumeDefect",
234
+ "WavelengthRange",
235
+ "Weight",
236
+ "Width",
237
+ "WorldAngle",
238
+ "WorldAngleInfo",
239
+ "XRayBeamHPos",
240
+ "XrayBeam",
241
+ "XrayBeamAttenuation",
242
+ "XrayBeamAttenuationInfo",
243
+ "XrayBeamDeviation",
244
+ "XrayBeamDeviationInfo",
245
+ "XrayBeamHAngle",
246
+ "XrayBeamHPosInfo",
247
+ "XrayBeamVAngle",
248
+ "XrayBeamVAngleInfo",
249
+ "XrayBeamVPos",
250
+ "XrayBeamVPosInfo",
251
+ "XraybeamHAngleInfo",
252
+ "ZCoordinate",
253
+ "ZSensor",
254
+ "assay",
255
+ "cuvette",
256
+ "deuterium lamp",
257
+ "electrical energy",
258
+ "fiat object part",
259
+ "has object",
260
+ "license",
261
+ "monochromator",
262
+ "non-contact probe",
263
+ "object aggregate",
264
+ "probe",
265
+ "process",
266
+ "quality",
267
+ "temporal region"
268
+ ],
269
+ "relations": [
270
+ "altLabel",
271
+ "broader",
272
+ "closeMatch",
273
+ "designates",
274
+ "disjointWith",
275
+ "exactMatch",
276
+ "has participant",
277
+ "hasBatchNumber",
278
+ "hasBuildJobName",
279
+ "hasDepth",
280
+ "hasExposureTime",
281
+ "hasGapDistance",
282
+ "hasHatchDistance",
283
+ "hasHeight",
284
+ "hasIdentifier",
285
+ "hasIndex",
286
+ "hasLaserPower",
287
+ "hasManufacturingMethod",
288
+ "hasMaterialComposition",
289
+ "hasName",
290
+ "hasOrientation",
291
+ "hasPointDistance",
292
+ "hasPreparedSurface",
293
+ "hasStorageCondition",
294
+ "hasTimeInterval",
295
+ "hasWeight",
296
+ "hasWidth",
297
+ "holds",
298
+ "inheres in",
299
+ "located in"
300
+ ],
301
+ "non_taxonomies": [
302
+ {
303
+ "ID": "NR_e5c11f0a",
304
+ "head": "Ratio Measurement Information Content Entity",
305
+ "tail": "Ratio Measurement Information Content Entity",
306
+ "relation": "exactMatch"
307
+ },
308
+ {
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+ "ID": "NR_cc383498",
310
+ "head": "SubstrateMaterial",
311
+ "tail": "Recipe",
312
+ "relation": "broader"
313
+ },
314
+ {
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+ "ID": "NR_1e491f70",
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+ "head": "XrayBeam",
317
+ "tail": "XraybeamHAngleInfo",
318
+ "relation": "inheres in"
319
+ },
320
+ {
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+ "ID": "NR_631daa15",
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+ "head": "BoardType",
323
+ "tail": "Sample",
324
+ "relation": "broader"
325
+ },
326
+ {
327
+ "ID": "NR_ed947884",
328
+ "head": "Encapsulant",
329
+ "tail": "PhotovoltaicModule",
330
+ "relation": "disjointWith"
331
+ },
332
+ {
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+ "ID": "NR_7344f3be",
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+ "head": "Scale",
335
+ "tail": "Recipe",
336
+ "relation": "broader"
337
+ },
338
+ {
339
+ "ID": "NR_c106a5b1",
340
+ "head": "XrayBeam",
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+ "tail": "XrayBeamAttenuationInfo",
342
+ "relation": "inheres in"
343
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344
+ {
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+ "ID": "NR_a9368d85",
346
+ "head": "ScanRate",
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+ "tail": "Recipe",
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+ "relation": "broader"
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+ },
350
+ {
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+ "ID": "NR_a4311bdd",
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+ "head": "PiezoelectricSensorLocation",
353
+ "tail": "Tool",
354
+ "relation": "broader"
355
+ },
356
+ {
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+ "ID": "NR_f9dd7ff6",
358
+ "head": "One Dimensional Extent",
359
+ "tail": "One Dimensional Extent",
360
+ "relation": "exactMatch"
361
+ },
362
+ {
363
+ "ID": "NR_350ba023",
364
+ "head": "ScanSpeed",
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+ "tail": "Recipe",
366
+ "relation": "broader"
367
+ },
368
+ {
369
+ "ID": "NR_03c95d77",
370
+ "head": "Designative Information Content Entity",
371
+ "tail": "Directive Information Content Entity",
372
+ "relation": "disjointWith"
373
+ },
374
+ {
375
+ "ID": "NR_e3851a7a",
376
+ "head": "ZCoordinate",
377
+ "tail": "AnalyticalResult",
378
+ "relation": "broader"
379
+ },
380
+ {
381
+ "ID": "NR_7f2de138",
382
+ "head": "CompressionStageTiltZInfo",
383
+ "tail": "CompressionStageTiltZ",
384
+ "relation": "designates"
385
+ },
386
+ {
387
+ "ID": "NR_dc96cc91",
388
+ "head": "SliceLinesPerLayerFile",
389
+ "tail": "LPBFO_00746",
390
+ "relation": "exactMatch"
391
+ },
392
+ {
393
+ "ID": "NR_fed9b9c1",
394
+ "head": "Computer",
395
+ "tail": "Computer",
396
+ "relation": "exactMatch"
397
+ },
398
+ {
399
+ "ID": "NR_b06e4cbe",
400
+ "head": "Sample",
401
+ "tail": "Depth",
402
+ "relation": "hasDepth"
403
+ },
404
+ {
405
+ "ID": "NR_1fa63f30",
406
+ "head": "SolventType",
407
+ "tail": "Recipe",
408
+ "relation": "broader"
409
+ },
410
+ {
411
+ "ID": "NR_bb9af387",
412
+ "head": "temporal region",
413
+ "tail": "temporal region",
414
+ "relation": "exactMatch"
415
+ },
416
+ {
417
+ "ID": "NR_19a65501",
418
+ "head": "InterdigitatedCombIdentifier",
419
+ "tail": "Sample",
420
+ "relation": "broader"
421
+ },
422
+ {
423
+ "ID": "NR_50b64787",
424
+ "head": "CellCutType",
425
+ "tail": "Sample",
426
+ "relation": "broader"
427
+ },
428
+ {
429
+ "ID": "NR_d8e1d886",
430
+ "head": "PilatusHumidityInfo",
431
+ "tail": "PilatusHumidity",
432
+ "relation": "designates"
433
+ },
434
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435
+ "ID": "NR_c67c318d",
436
+ "head": "ScanLines",
437
+ "tail": "Recipe",
438
+ "relation": "broader"
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+ },
440
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441
+ "ID": "NR_0aa36d48",
442
+ "head": "Designative Name",
443
+ "tail": "Designative Name",
444
+ "relation": "exactMatch"
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+ },
446
+ {
447
+ "ID": "NR_c49d7969",
448
+ "head": "Sample",
449
+ "tail": "InterFeatureCoolingTime",
450
+ "relation": "hasTimeInterval"
451
+ },
452
+ {
453
+ "ID": "NR_0e71cc94",
454
+ "head": "SAXSPeltierPowerInfo",
455
+ "tail": "SAXSPeltierPower",
456
+ "relation": "designates"
457
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458
+ {
459
+ "ID": "NR_fa8be1d1",
460
+ "head": "Height",
461
+ "tail": "Height",
462
+ "relation": "exactMatch"
463
+ },
464
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465
+ "ID": "NR_10cd2450",
466
+ "head": "RunStartDate",
467
+ "tail": "Recipe",
468
+ "relation": "broader"
469
+ },
470
+ {
471
+ "ID": "NR_1bb091da",
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+ "head": "Date Identifier",
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+ "tail": "Date Identifier",
474
+ "relation": "exactMatch"
475
+ },
476
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477
+ "ID": "NR_c43b8b1f",
478
+ "head": "Sample",
479
+ "tail": "LaserPower",
480
+ "relation": "hasLaserPower"
481
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482
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483
+ "ID": "NR_248f6522",
484
+ "head": "electrical energy",
485
+ "tail": "electrical energy",
486
+ "relation": "exactMatch"
487
+ },
488
+ {
489
+ "ID": "NR_5157ffd9",
490
+ "head": "Computing Artifact Function",
491
+ "tail": "Computing Artifact Function",
492
+ "relation": "exactMatch"
493
+ },
494
+ {
495
+ "ID": "NR_367d6859",
496
+ "head": "Code Identifier",
497
+ "tail": "Code Identifier",
498
+ "relation": "exactMatch"
499
+ },
500
+ {
501
+ "ID": "NR_e7ff179d",
502
+ "head": "WorldAngleInfo",
503
+ "tail": "WorldAngle",
504
+ "relation": "designates"
505
+ },
506
+ {
507
+ "ID": "NR_be11d5cc",
508
+ "head": "IntegralGain",
509
+ "tail": "Tool",
510
+ "relation": "broader"
511
+ },
512
+ {
513
+ "ID": "NR_afd355d3",
514
+ "head": "Sample",
515
+ "tail": "HatchDistance",
516
+ "relation": "hasHatchDistance"
517
+ },
518
+ {
519
+ "ID": "NR_57bae175",
520
+ "head": "XrayBeamHPosInfo",
521
+ "tail": "XRayBeamHPos",
522
+ "relation": "designates"
523
+ },
524
+ {
525
+ "ID": "NR_91d9c978",
526
+ "head": "quality",
527
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+ },
1784
+ {
1785
+ "ID": "NR_1d44ffe0",
1786
+ "head": "ManufacturingMethod",
1787
+ "tail": "ProcessingMethod",
1788
+ "relation": "altLabel"
1789
+ },
1790
+ {
1791
+ "ID": "NR_d42d6d12",
1792
+ "head": "Maximum Ordinal Measurement Information Content Entity",
1793
+ "tail": "Maximum Ordinal Measurement Information Content Entity",
1794
+ "relation": "exactMatch"
1795
+ },
1796
+ {
1797
+ "ID": "NR_3e142c78",
1798
+ "head": "LineDefect",
1799
+ "tail": "PlanarDefect",
1800
+ "relation": "disjointWith"
1801
+ },
1802
+ {
1803
+ "ID": "NR_4b8bd22a",
1804
+ "head": "AFMInstrumentModel",
1805
+ "tail": "Tool",
1806
+ "relation": "broader"
1807
+ },
1808
+ {
1809
+ "ID": "NR_d370bef0",
1810
+ "head": "process",
1811
+ "tail": "process",
1812
+ "relation": "exactMatch"
1813
+ },
1814
+ {
1815
+ "ID": "NR_f4bdcbe7",
1816
+ "head": "SampleSize",
1817
+ "tail": "Sample",
1818
+ "relation": "broader"
1819
+ },
1820
+ {
1821
+ "ID": "NR_87408aa9",
1822
+ "head": "license",
1823
+ "tail": "license",
1824
+ "relation": "exactMatch"
1825
+ },
1826
+ {
1827
+ "ID": "NR_092da6c3",
1828
+ "head": "Designative Information Content Entity",
1829
+ "tail": "Descriptive Information Content Entity",
1830
+ "relation": "disjointWith"
1831
+ },
1832
+ {
1833
+ "ID": "NR_46ee0a18",
1834
+ "head": "ZSensor",
1835
+ "tail": "Tool",
1836
+ "relation": "broader"
1837
+ },
1838
+ {
1839
+ "ID": "NR_993a2df3",
1840
+ "head": "Act of Data Transformation",
1841
+ "tail": "Act of Data Transformation",
1842
+ "relation": "exactMatch"
1843
+ },
1844
+ {
1845
+ "ID": "NR_b377cb83",
1846
+ "head": "XrayBeam",
1847
+ "tail": "XrayBeamHPosInfo",
1848
+ "relation": "located in"
1849
+ },
1850
+ {
1851
+ "ID": "NR_b135f418",
1852
+ "head": "Turn",
1853
+ "tail": "Recipe",
1854
+ "relation": "broader"
1855
+ }
1856
+ ]
1857
+ }
mdsonto/type_taxonomies.json ADDED
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