4. PROVOLONE recipes
This section contains a series of practical recipes and examples to exemplify how digital reading pipelines can be semantically modelled by following the principles outlined in Section 3.
Digital reading pipelines are broken down into a small number of core entities:
- a pipeline is made of distinct steps, where the output of one step often serves as output to the following one;
- a pipeline is typically built in the context of a specific project, but the models and software it relies on may be reused within several pipelines across different projects;
- a pipeline uses, consumes, and produces digital objects (e.g., data, machine learning models, scripts, Jupyter notebooks).
For each of these entities, we define a model, consisting of its semantic fields (i.e., properties), and we provide examples in the form of JSON(-LD) of how these models and their fields should be mapped into semantic statements (RDF).
Finally, in the section URI templates we provide some guidance for constructing URIs for specific entity types.
Generic models¶
Project¶
Description: A project is the context within which a digital reading pipeline is created and implemented. (See model documentation on Zellij).
Fields:
- Name: The name of the project (LAF.6)
- Actor: A person or institution participating to the project (SRDF.129)
- Actor role: The role played by a given person or institution in the project (SRDF.130)
- Description: A description of the project (LAF.15)
- URL: The URL of an online resource providing further information about the project (SRDF.369)
Example
The BSO image classification pipeline was created by Florian Kräutli (knowledge graph engineer) in the context of the Bilder der Schweiz Online (BSO) project (https://www.sari.uzh.ch/en/Projects/bilder-der-schweiz-online.html). The »Bilder der Schweiz online« (Images of Switzerland) initiative is a three-year project at the University of Zurich (2020-2022), jointly undertaken by Swiss Art Research Infrastructure (SARI) and the lecturer for Swiss Art and Museology at the Institute of Art History at the University of Zurich.
JSON-LD
{
"@context": [
"https://linked.art/ns/v1/linked-art.json",
{
"crmdig": "http://www.ics.forth.gr/isl/CRMdig/",
"DigitalObject":"crmdig:D1_Digital_Object"
},
{
"crmpe": "http://parthenos.d4science.org/CRMext/CRMpe.rdfs/",
"Project": "crmpe:PE35_Project"
},
{
"aaao": "https://ontology.swissartresearch.net/aaao/"
},
{
"crm":"http://www.cidoc-crm.org/cidoc-crm/",
"has_dependency":"crm:P20_had_specific_purpose"
},
{
"ex":"https://examples.swissartresearch.net/"
}
],
"id": "ex:project/1234",
"type": "Project",
"identified_by":[
{
"type": "Name",
"id": "ex:project/1234/appellation/1",
"content": "Bilder der Schweiz Online (BSO)"
},
{
"type": "Identifier",
"id": "ex:project/1234/appellation/2",
"content": "https://www.sari.uzh.ch/en/Projects/bilder-der-schweiz-online.html",
"classified_as":{
"type": "Type",
"id": "https://vocab.getty.edu/aat/300404630",
"rdfs:label":"URL"
}
}
],
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:project/1234/linguisticobject/1",
"content": "The »Bilder der Schweiz online« (Images of Switzerland) initiative is a three-year project at the University of Zurich (2020-2022), jointly undertaken by Swiss Art Research Infrastructure (SARI) and the lecturer for Swiss Art and Museology at the Institute of Art History at the University of Zurich. "
},
"crm:P01i_is_domain_of":{
"type":"crm:PC14_carried_out_by",
"id":"XXXXXX",
"crm:P02_has_range":{
"type":"Actor",
"id":"ex:person/1",
"identified_by":{
"type": "Name",
"id": "ex:person/1/appellation/1",
"content": "Florian Kräutli"
}
},
"crm:P14.1_in_the_role_of":{
"type":"Type",
"rdfs:label":"Knowledge Graph Engineer",
"id": "XXXXXX"
}
}
}
Digital object¶
Description: The digital object that is consumed/generated/used by a digital reading pipeline. (See model documentation on Zellij).
Fields:
- Name: The name of the digital object
- Identifier: The identifier of the digital object
- Type: The kind of digital object
- Description: A description of the digital object
- Part of dataset: The dataset of which a given digital object is part
Example
The CSV file at https://raw.githubusercontent.com/swiss-art-research-net/bso-image-classification/refs/heads/main/data/imageAnnotations.csv which contains labeled data for image classification in the BSO project.
JSON-LD
{
"@context": [
"https://linked.art/ns/v1/linked-art.json",
{
"crmdig": "http://www.ics.forth.gr/isl/CRMdig/",
"DigitalObject":"crmdig:D1_Digital_Object"
},
{
"ex":"https://examples.swissartresearch.net/"
}
],
"id": "digitalobject/5678",
"type": "DigitalObject",
"identified_by":[
{
"type": "Name",
"id": "ex:digitalobject/5678/appellation/1",
"content": "data/imageAnnotations.csv"
},
{
"type": "Identifier",
"id": "ex:digitalobject/5678/appellation/2",
"content": "https://raw.githubusercontent.com/swiss-art-research-net/bso-image-classification/refs/heads/main/data/imageAnnotations.csv",
"classified_as":{
"type": "Type",
"id": "https://vocab.getty.edu/aat/300404630",
"rdfs:label":"URL"
}
}
],
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:digitalobject/5678/linguisticobject/1",
"content": "CSV file containing labeled data for image classification in the BSO project."
}
}
Software¶
Description: A kind of digital object that is designed to be executed by a computing device and provide an algorithm for it to execute some function(s). (See model documentation on Zellij).
Fields:
- Name: The name of the software
- Type: The kind of software (remove?)
- Identifier: An identifier for the software
- Description: A description of the software
- URL: The URL of an online resource providing information about the project or containing the software itself.
Example
The Jupyter notebook available at https://github.com/swiss-art-research-net/bso-image-classification/blob/main/notebooks/model%20training.ipynb that was used to train the BSO image classification model.
JSON-LD
{
"@context": [
"https://linked.art/ns/v1/linked-art.json",
{
"crmdig": "http://www.ics.forth.gr/isl/CRMdig/",
"Software":"crmdig:D14_Software"
},
{
"crm":"http://www.cidoc-crm.org/cidoc-crm/",
"has_dependency":"crm:P20_had_specific_purpose"
},
{
"ex":"https://examples.swissartresearch.net/"
}
],
"id": "ex:software/1234",
"type": "Software",
"identified_by":[
{
"type": "Name",
"id": "ex:digitalobject/101112/appellation/1",
"content": "model training.ipynb"
},
{
"type": "Identifier",
"id": "ex:digitalobject/101112/appellation/2",
"content": "https://github.com/swiss-art-research-net/bso-image-classification/blob/main/notebooks/model%20training.ipynb",
"classified_as":{
"type": "Type",
"id": "https://vocab.getty.edu/aat/300404630",
"rdfs:label":"URL"
}
}
],
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:software/1/linguisticobject/1",
"content": "The Jupyter notebook that was used to train the BSO image classification model."
}
}
Service¶
Description: Any hosted service that can be used to perform tasks related to a digital reading pipeline, such as data labelling, analysis, processing, etc. (See model documentation on Zellij).
Fields:
- Name: The name of the service
- Type: The type of service, typically expressed by means of a controlled vocabulary
- Description: A description of the service
- Actor: A person or institution involved in activities related to the service (e.g. development, maintenance, hosting, etc.)
- Actor Role: The role played by a given person or institution with respect to the service
- URI: The URI of the service
Example
The SPARQL-based API for image similarity detection developed by Florian Kräutli for the gta project, and available at https://researchportal-staging.gta.arch.ethz.ch/sparql.
JSON-LD
{
"@context": [
"https://linked.art/ns/v1/linked-art.json",
{
"crmdig": "http://www.ics.forth.gr/isl/CRMdig/",
"DigitalObject":"crmdig:D1_Digital_Object"
},
{
"crmpe": "http://parthenos.d4science.org/CRMext/CRMpe.rdfs/",
"Project": "crmpe:PE35_Project"
},
{
"aaao": "https://ontology.swissartresearch.net/aaao/"
},
{
"crm":"http://www.cidoc-crm.org/cidoc-crm/",
"has_dependency":"crm:P20_had_specific_purpose"
},
{
"ex":"https://examples.swissartresearch.net/"
}
],
...
}
Pipeline-related models¶
Generic pipeline step¶
Generic fields:
- Type: The type of the pipeline step, typically expressed by means of a controlled vocabulary (LAF.11)
- Name: The name of the pipeline step (LAF.6)
- Description: A brief description of the pipeline step (LAF.15)
- Timestamp – begin: The timestamp of the date when a given pipeline step started (LAF.25)
- Timestamp – end: The timestamp of the date when a given pipeline step ended (LAF.26)
- Part of project: The project within which the pipeline step was carried out (LAF.42)
- Dependency: The subsequent step in the pipeline sequence (PROF.3)
Labelling step¶
Description: The step in a digital reading pipeline that involves creating manually labelled examples for evaluating or training a machine learning model. (See model documentation on Zellij).
Specific fields (in addition to the generic ones):
- Tool: The digital tool to perform data labeling (SEMF.133)
- Input: The input digital objects (e.g. texts, images, etc.) on which labeling is performed (SEMF.35)
- Output: A collection of manually labeled digital objects. The type of labels directly depends on the ML task at hand (SEMF.34)
- Log file: Optionally, the labeling process may produce a log file where system and user actions are logged (ANTF.1)
- Annotator: The human annotator(s) involved in the labeling process (LAF.21)
- Tool: The digital tool to perform data labeling (SEMF.133)
- Platform used: The platform where a given tool is hosted and offered as a service (PROF.20)
Example
The image labelling step of the digital reading pipeline developed by SARI for the Bilder der Schweiz Online (BSO) project.
JSON-LD
{
"@context": [
"https://linked.art/ns/v1/linked-art.json",
{
"crmdig": "http://www.ics.forth.gr/isl/CRMdig/",
"DigitalObject":"crmdig:D1_Digital_Object",
"DigitalMachineEvent": "crmdig:D7_Digital_Machine_Event"
},
{
"crmpe": "http://parthenos.d4science.org/CRMext/CRMpe.rdfs/",
"Project": "crmpe:PE35_Project"
},
{
"aaao": "https://ontology.swissartresearch.net/aaao/",
"DigitalReading": "aaao:ZE17_Digital_Reading"
},
{
"crm":"http://www.cidoc-crm.org/cidoc-crm/",
"part_of": "crm:P9i_forms_part_of",
"dependency": "crm:P20_had_specific_purpose",
"input": "crm:L10_had_input",
"output": "crm:L11_had_output"
},
{
"ex":"https://examples.swissartresearch.net/",
"provoc":"https://vocabulary.swissartresearch.net/provolone/"
}
],
"id":"ex:/digitalreading/1234",
"type":"DigitalReading",
"_label": "BSO Image Labeling",
"begin_of_the_begin":"2023-05-12T10:15:30Z",
"end_of_the_end": "2023-05-12T10:15:30Z",
"classified_as": "provoc:/Labelling",
"part_of": {
"id": "ex:project/1234",
"type": "Project",
"identified_by":[
{
"type": "Name",
"id": "ex:project/1234/appellation/1",
"content": "Bilder der Schweiz Online (BSO)"
},
{
"type": "Identifier",
"id": "ex:project/1234/appellation/2",
"content": "https://www.sari.uzh.ch/en/Projects/bilder-der-schweiz-online.html",
"classified_as":{
"type": "Type",
"id": "https://vocab.getty.edu/aat/300404630",
"rdfs:label":"URL"
}
}
]
},
"input":{
"id": "digitalobject/1234",
"type": "DigitalObject",
"identified_by":[
{
"type": "Name",
"id": "ex:digitalobject/1234/appellation/1",
"content": "images/"
},
{
"type": "Identifier",
"id": "ex:digitalobject/1234/appellation/2",
"content": "https://raw.githubusercontent.com/swiss-art-research-net/bso-image-classification/refs/heads/main/images/",
"classified_as":{
"type": "Type",
"id": "https://vocab.getty.edu/aat/300404630",
"rdfs:label":"URL"
}
}
],
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:digitalobject/1234/linguisticobject/1",
"content": "Folder containing downloaded images."
}
}
,
"output": {
"id": "digitalobject/5678",
"type": "DigitalObject",
"identified_by":[
{
"type": "Name",
"id": "ex:digitalobject/5678/appellation/1",
"content": "data/imageAnnotations.csv"
},
{
"type": "Identifier",
"id": "ex:digitalobject/5678/appellation/2",
"content": "https://raw.githubusercontent.com/swiss-art-research-net/bso-image-classification/refs/heads/main/data/imageAnnotations.csv",
"classified_as":{
"type": "Type",
"id": "https://vocab.getty.edu/aat/300404630",
"rdfs:label":"URL"
}
}
],
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:digitalobject/5678/linguisticobject/1",
"content": "CSV file containing labeled data for image classification in the BSO project."
}
},
"dependency": {
"id": "ex:/digitalreading/1234",
"_label": "BSO Model Training",
"classified_as": "provoc:ModelTraining"
}
}
Model training step¶
Description: The step of a digital reading pipeline that consists in training a machine learning statistical model for performing a given task, typically by means of a manually labelled dataset (See model documentation on Zellij).
Fields:
- Input: The labeled data (e.g. texts, images, etc.) that was used to train the model (SEMF.35)
- Output: The trained model produced (SEMF.34)
- Log file: Optionally, the model training process may produce a log file (PROF.1)
- Code: Script/notebook used to train the model. (SEMF.133)
- Service: External platform used to train the model (e.g. Roboflow Train) (PROF.20)
Example
The model training step of the digital reading pipeline developed by SARI for the Bilder der Schweiz Online (BSO) project. A dataset of manually labelled images, contained in the
...folder, is used to train the model, and the trained model is then saved at....
JSON-LD
{
"@context": [
"https://linked.art/ns/v1/linked-art.json",
{
"crmdig": "http://www.ics.forth.gr/isl/CRMdig/",
"DigitalObject":"crmdig:D1_Digital_Object",
"DigitalMachineEvent": "crmdig:D7_Digital_Machine_Event"
},
{
"crmpe": "http://parthenos.d4science.org/CRMext/CRMpe.rdfs/",
"Project": "crmpe:PE35_Project"
},
{
"aaao": "https://ontology.swissartresearch.net/aaao/",
"DigitalReading": "aaao:ZE17_Digital_Reading"
},
{
"crm":"http://www.cidoc-crm.org/cidoc-crm/",
"part_of": "crm:P9i_forms_part_of",
"dependency": "crm:P20_had_specific_purpose",
"input": "crm:L10_had_input",
"output": "crm:L11_had_output"
},
{
"ex":"https://examples.swissartresearch.net/",
"provoc":"https://vocabulary.swissartresearch.net/provolone/"
}
],
"id":"ex:/digitalreading/5678",
"type":"DigitalReading",
"_label": "BSO Image Classification Model Training Step",
"begin_of_the_begin":"2023-05-12T10:15:30Z",
"end_of_the_end": "2023-05-12T10:19:30Z",
"classified_as": "provoc:/Training",
"part_of": {
"id": "ex:project/1234",
"type": "Project",
"identified_by":[
{
"type": "Name",
"id": "ex:project/1234/appellation/1",
"content": "Bilder der Schweiz Online (BSO)"
},
{
"type": "Identifier",
"id": "ex:project/1234/appellation/2",
"content": "https://www.sari.uzh.ch/en/Projects/bilder-der-schweiz-online.html",
"classified_as":{
"type": "Type",
"id": "https://vocab.getty.edu/aat/300404630",
"rdfs:label":"URL"
}
}
]
},
"code": {
"id": "digitalobject/101112",
"type": "Software",
"identified_by":[
{
"type": "Name",
"id": "ex:project/101112/appellation/1",
"content": "model training.ipynb"
},
{
"type": "Identifier",
"id": "ex:project/101112/appellation/2",
"content": "https://github.com/swiss-art-research-net/bso-image-classification/blob/main/notebooks/model%20training.ipynb",
"classified_as":{
"type": "Type",
"id": "https://vocab.getty.edu/aat/300404630",
"rdfs:label":"URL"
}
}
],
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:digitalobject/101112/linguisticobject/1",
"content": "The Jupyter notebooked used to train the model for the BSO image classification pipeline."
}
},
"input":{
"id": "digitalobject/5678",
"type": "DigitalObject",
"identified_by":[
{
"type": "Name",
"id": "ex:digitalobject/5678/appellation/1",
"content": "data/imageAnnotations.csv"
}
]
}
,
"output": {
"id": "digitalobject/91011",
"type": "Software",
"identified_by":[
{
"type": "Name",
"id": "ex:digitalobject/91011/appellation/1",
"content": "model.pkl"
},
{
"type": "Identifier",
"id": "ex:digitalobject/91011/appellation/2",
"content": "https://github.com/swiss-art-research-net/bso-image-classification/blob/main/models/model.pkl",
"classified_as":{
"type": "Type",
"id": "https://vocab.getty.edu/aat/300404630",
"rdfs:label":"URL"
}
}
],
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:digitalobject/91011/linguisticobject/1",
"content": "Image classification model trained on manually labelled BSO data."
}
},
"dependency": {
"id": "ex:/digitalreading/91011",
"_label": "BSO Image Classification Prediction",
"classified_as": "provoc:Prediction"
}
}
Prediction step¶
Description: The step of a digital reading pipeline at which a statistical machine learning model makes predictions on unseen data (See model documentation on Zellij).
Fields:
- Input: A set of input digital objects (SEMF.35)
- Output: Digital object(s) containing the model's predictions (SEMF.34)
- Log file: Optionally, the prediction process may produce a log file (ANTF.1)
- Code: Script/notebook used to obtain the predictions (SEMF.133)
- Model: The trained model used to generate predictions (SEMF.35)
- API: External API service used to obtain the predictions, as an alternative to a (local) model (PROF.20).
Example
The prediction step of the BSO image classification pipeline; it takes as input the images at
images/, it classifies them by using the trained modelmodel.pklfor inference, and finally writes the predictions to fileoutput/predictions.csv.
JSON-LD
{
"@context": [
"https://linked.art/ns/v1/linked-art.json",
{
"crmdig": "http://www.ics.forth.gr/isl/CRMdig/",
"DigitalObject":"crmdig:D1_Digital_Object",
"DigitalMachineEvent": "crmdig:D7_Digital_Machine_Event"
},
{
"crmpe": "http://parthenos.d4science.org/CRMext/CRMpe.rdfs/",
"Project": "crmpe:PE35_Project"
},
{
"aaao": "https://ontology.swissartresearch.net/aaao/",
"DigitalReading": "aaao:ZE17_Digital_Reading"
},
{
"crm":"http://www.cidoc-crm.org/cidoc-crm/",
"part_of": "crm:P9i_forms_part_of",
"dependency": "crm:P20_had_specific_purpose",
"input": "crm:L10_had_input",
"output": "crm:L11_had_output",
"model": "crm:L10_had_input",
"code": "crm:L23_used_software_or_firmware"
},
{
"ex":"https://examples.swissartresearch.net/",
"provoc":"https://vocabulary.swissartresearch.net/provolone/"
}
],
"id":"ex:digitalreading/101112",
"type":"DigitalReading",
"_label": "BSO Image Classification Prediction",
"begin_of_the_begin":"2023-05-12T10:15:30Z",
"end_of_the_end": "2023-05-12T10:19:30Z",
"classified_as": "provoc:/Prediction",
"part_of": {
"id": "ex:project/1234",
"type": "Project",
"identified_by":[
{
"type": "Name",
"id": "ex:project/1234/appellation/1",
"content": "Bilder der Schweiz Online (BSO)"
},
{
"type": "Identifier",
"id": "ex:project/1234/appellation/2",
"content": "https://www.sari.uzh.ch/en/Projects/bilder-der-schweiz-online.html",
"classified_as":{
"type": "Type",
"id": "https://vocab.getty.edu/aat/300404630",
"rdfs:label":"URL"
}
}
]
},
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:/digitalreading/101112/linguisticobject/1",
"content": "Prediction of image classes on unseen data from the BSO project."
},
"model": {
"id": "ex:digitalobject/91011"
},
"code": {
"id": "ex:software/91011",
"type": "Software",
"identified_by":[
{
"type": "Name",
"id": "ex:software/91011/appellation/1",
"content": "classify images.ipynb"
},
{
"type": "Identifier",
"id": "ex:software/91011/appellation/2",
"content": "https://github.com/swiss-art-research-net/bso-image-classification/blob/main/notebooks/classify%20images.ipynb",
"classified_as": {"id": "https://vocab.getty.edu/aat/300404630"}
}
],
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:software/91011/linguisticobject/1",
"content": "The Jupyter notebook that was used to run inference on unseen BSO images by using the trained model."
}
},
"input": {
"id": "ex:digitalobject/1234"
},
"output": {
"id": "ex:digitalobject/151617",
"type": "DigitalObject",
"identified_by":[
{
"type": "Name",
"id": "ex:digitalobject/151617/appellation/1",
"content": "output/predictions.csv"
},
{
"type": "Identifier",
"id": "ex:digitalobject/151617/appellation/2",
"content": "https://github.com/swiss-art-research-net/bso-image-classification/blob/main/output/predictions.csv",
"classified_as":{
"id": "https://vocab.getty.edu/aat/300404630"
}
}
],
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:digitalobject/151617linguisticobject/1",
"content": "The file where the image classification results are written."
}
}
}
Data transformation step¶
Description: The step of a digital reading pipeline that consists in transforming data into a derivative form (e.g., format conversion, binarisation of images, etc.), often performed as a pre-processing step (See model documentation on Zellij).
Specific fields (in addition to the generic ones):
- Input: A set of digital objects to transform (SEMF.35)
- Output: A derivative version of the input digital objects (SEMF.34)
- Log file: Optionally, the prediction process may produce a log file (ANTF.1)
- Code: Script/notebook used to perform the data transformation (SEMF.133)
- API: External API service used to perform the data transformation (PROF.20).
Example
In the BSO image classification pipeline, the CSV file contaning the model's predictions (
output/predictions.csv) is transformed into RDF format (output/classifications.trig) by means of the Jupyter notebooknotebooks/create RDF output.ipynb.
JSON-LD
{
"@context": [
"https://linked.art/ns/v1/linked-art.json",
{
"crmdig": "http://www.ics.forth.gr/isl/CRMdig/",
"DigitalObject":"crmdig:D1_Digital_Object",
"DigitalMachineEvent": "crmdig:D7_Digital_Machine_Event"
},
{
"crmpe": "http://parthenos.d4science.org/CRMext/CRMpe.rdfs/",
"Project": "crmpe:PE35_Project"
},
{
"aaao": "https://ontology.swissartresearch.net/aaao/",
"DigitalReading": "aaao:ZE17_Digital_Reading"
},
{
"crm":"http://www.cidoc-crm.org/cidoc-crm/",
"part_of": "crm:P9i_forms_part_of",
"dependency": "crm:P20_had_specific_purpose",
"input": "crm:L10_had_input",
"output": "crm:L11_had_output",
"code": "crm:L23_used_software_or_firmware"
},
{
"ex":"https://examples.swissartresearch.net/",
"provoc":"https://vocabulary.swissartresearch.net/provolone/"
}
],
"id":"ex:digitalreading/101112",
"type":"DigitalMachineEvent",
"_label": "BSO Image Classification pipeline – CSV to RDF conversion",
"begin_of_the_begin":"2023-05-12T10:15:30Z",
"end_of_the_end": "2023-05-12T10:19:30Z",
"classified_as": "provoc:/Transformation",
"part_of": {
"id": "ex:project/1234",
"type": "Project",
"identified_by":[{
"type": "Name",
"id": "ex:project/1234/appellation/1",
"content": "Bilder der Schweiz Online (BSO)"
}]
},
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:/digitalreading/101112/linguisticobject/1",
"content": "Conversion of model's predictions from CSV to RDF format"
},
"code": {
"id": "ex:software/5678",
"type": "Software",
"identified_by":[
{
"type": "Name",
"id": "ex:digitalobject/5678/appellation/1",
"content": "create RDF output.ipynb"
},
{
"type": "Identifier",
"id": "ex:digitalobject/5678/appellation/2",
"content": "https://github.com/swiss-art-research-net/bso-image-classification/blob/main/notebooks/create%20RDF%20output.ipynb",
"classified_as": {"id": "https://vocab.getty.edu/aat/300404630"}
}
],
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:software/5678/linguisticobject/1",
"content": "The Jupyter notebook that was used to convert the model's predictions from CSV to RDF format."
}
},
"input": {
"id": "ex:digitalobject/151617",
"type": "DigitalObject",
"identified_by":[
{
"type": "Name",
"id": "ex:digitalobject/151617/appellation/1",
"content": "output/predictions.csv"
}
],
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:digitalobject/151617linguisticobject/1",
"content": "The CSV file where the image classification results are written."
}
},
"output": {
"id": "ex:digitalobject/181920",
"type": "DigitalObject",
"identified_by":[
{
"type": "Name",
"id": "ex:digitalobject/181920/appellation/1",
"content": "output/classifications.trig"
},
{
"type": "Identifier",
"id": "ex:digitalobject/181920/appellation/2",
"content": "https://github.com/swiss-art-research-net/bso-image-classification/blob/main/output/classifications.trig",
"classified_as": {"id": "https://vocab.getty.edu/aat/300404630"}
}
],
"referred_to_by":{
"type": "LinguisticObject",
"id": "ex:digitalobject/181920/linguisticobject/1",
"content": "The converted file where the image classification results in RDF format are written."
}
}
}
Data analysis step¶
🚧 This section is still work-in-progress 🔜