PROVOLONE: the PROVenance Ontology for Linked Open pipeliNEs
Project Documentation
Models Items
18
Digital Object
Digital Reading - Labeling
Digital Reading - Data Analysis
Digital Reading - Data Transformation
Digital Reading - Model Training
Digital Reading - Prediction
Enrichment - Attributed Dimension
Enrichment - Classification
Enrichment - GeoReferencing - Creation Place
Enrichment - GeoReferencing - Generic
Enrichment - Image Segmentation - by Referent Classification
Enrichment - Similarity
Group
Person
Place
Project
Service
Software
PROVOLONE: the PROVenance Ontology for Linked Open pipeliNEs
Models
Digital Object
The digital object that is consumed/generated/used by a digital reading pipeline.
This is a minimal model for tracking basic metadata. To model full digital object information see Linked.Art or SRDM 2.0 digital object semantic data models.
Examples:
- The BSO image `zbz-990102318630205508`
- The `prediction.csv` output file generated by the BSO classification pipeline
System Name:
digital_object
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http://takin.solutions/space/provolone/composite/PROM.7
Enrichment - Similarity
The computed similarity between two digital objects (e.g. images, texts, etc.) belonging to the same type.
Examples:
- The atttribution of a similarity score of 87.7% between image `cms-182741` and image `cms-132259`, as returned by the GTA SPARQL API.
System Name:
enrichment_similarity
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http://takin.solutions/space/provolone/composite/PROM.11
Digital Reading - Data Analysis
The extraction of information from a digital object carried out by analysing the object itself.
Examples:
- The extraction of color scheme information from images
System Name:
digital_reading_data_analysis
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http://takin.solutions/space/provolone/composite/PROM.6
Digital Reading - Labeling
The creation of a manually labelled dataset with the purpose of training and/or evaluating a machine learning model to perform a given task.
Examples:
- The labelling of BSO images for the image classification task.
System Name:
digital_reading_labeling
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http://takin.solutions/space/provolone/composite/PROM.2
Enrichment - GeoReferencing - Generic
The attribution of a place which is somehow referenced or evoked by the image.
Examples:
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System Name:
enrichment_geo_referencing_generic
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http://takin.solutions/space/provolone/composite/PROM.18
Service
Any hosted service used in a digital reading pipeline. Example of services: APIs to perform NLP or CV tasks, platforms where computations can be run (e.g. JupyterLab), or applications for data labelling.
Examples:
- The hosting of a SPARQL API by SARI to expose a service for computing image similarity
- The OpenAI API to expose various Large Language Models (LLMs)
System Name:
service
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2025-07-30T10:41:19.000Z
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http://takin.solutions/space/provolone/composite/PROM.1
Person
An individual actor participating in a digital reading project.
This is a minimal model for tracking basic metadata. To model full person information see Linked.Art or SRDM 2.0 person semantic data models.
Examples:
- The annotator of images in the BSO project.
System Name:
person
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http://takin.solutions/space/provolone/composite/PROM.8
Software
The software model enables the documentation of digital objects that are designed to be executed by a computing device and provide an algorithm for it to execute some function(s).
This is a minimal model for tracking basic metadata.
Examples:
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System Name:
software
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http://takin.solutions/space/provolone/composite/PROM.17
Enrichment - Attributed Dimension
A calculated dimension of an image produced through automated analysis. For example, a detected colour.
Examples:
Colour attribution example
System Name:
enrichment_attributed_dimension
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http://takin.solutions/space/provolone/composite/PROM.14
Group
A group of individuals participating in a digital reading project.
This is a minimal model for tracking basic metadata. To model full group information see Linked.Art or SRDM 2.0 group semantic data models.
Examples:
- The team at SARI/UZH (considered as a group).
System Name:
group
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http://takin.solutions/space/provolone/composite/PROM.9
Project
A project is the context within which a digital reading pipeline is created and implemented.
Examples:
- The BSO pipeline for image classification was developed as part of the Bilder der Schweiz Online (BSO) project.
System Name:
project
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http://takin.solutions/space/provolone/composite/PROM.15
Digital Reading - Data Transformation
The transformation of a collection of data into a derivative format.
Examples:
- The conversion of BSO images from TIFF to PNG format
System Name:
digital_reading_data_transformation
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http://takin.solutions/space/provolone/composite/PROM.5
Enrichment - Image Segmentation - by Referent Classification
A model for documenting the referential status of an image segment created via a calculated computing process.
Examples:
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System Name:
enrichment_image_segmentation_by_referent_classification
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http://takin.solutions/space/provolone/composite/PROM.19
Place
A place in the sense of a geographic area, typically projected on earth (although not necessarily, could be relative to any body). The typical function of this model is to document a place which an image or digital object is linked to either through a referential connection or through a provenance (origin of image) connection.
This is a minimal model for tracking basic metadata. To model full place information see Linked.Art or SRDM 2.0 place semantic data models.
Examples:
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System Name:
place
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http://takin.solutions/space/provolone/composite/PROM.16
Digital Reading - Prediction
The prediction made by a statistical machine learning model on unseen data.
Examples:
- Image classification of BSO images
System Name:
digital_reading_prediction
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http://takin.solutions/space/provolone/composite/PROM.4
Enrichment - Classification
The classification status of a digital object (e.g. image, text, etc.) according to a pre-defined taxonomy.
Examples:
- The attribution of the classification label `landscape` to BSO image `zbz-990102318630205508` with a confidence of 0.099732
System Name:
enrichment_classification
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http://takin.solutions/space/provolone/composite/PROM.10
Digital Reading - Model Training
The training of a machine learning statistical model for performing a given task, typically by means of a manually labelled dataset.
Examples:
- The fine-tuning of a Resnet50 model for image classification by using xyz labelled images as training data
System Name:
digital_reading_model_training
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http://takin.solutions/space/provolone/composite/PROM.3
Enrichment - GeoReferencing - Creation Place
The attribution of a place where the image was created to a digital object.
Examples:
BSO place attribution for location of creation
System Name:
enrichment_geo_referencing_creation_place
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http://takin.solutions/space/provolone/composite/PROM.13