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The **Data Quality Assessment Ontology (DQA)** is a suggested extension of the [Data Quality Vocabulary (DQV)](https://www.w3.org/ns/dqv#) designed to enhance the assessment of data quality (DQ). It introduces structured evaluation processes, traceability of applied methods, granularities for assessments, and results of applying DQ indicators.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
Below is a diagram that represents the DQ Assessment Ontology (DQA):
## Add your files
<palign="center">
<imgsrc="dqa-ontology.png"alt="DQA Overview">
</p>
-[ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
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### Ontology Classes
The ontology defines the following key classes:
-**QualityAssessmentProcess**: Represents the process of evaluating DQ.
-**AssessmentGranularity**: Represents the level at which DQ is assessed (e.g., unique observation, set of observations).
-**AssessmentLabel**: Categorizes the result of a quality evaluation (e.g., "High Quality", "Medium Quality").
-**AssessmentMethod**: Defines the method used for DQ assessment, either formula-based or source-based.
-**FormulaBasedMethod**: A specific type of assessment method based on predefined formulas.
-**SourceBasedMethod**: A specific type of assessment method based on the source of the data.
### Object Properties
The ontology includes the following object properties to define relationships between the classes:
-**assessesIndicator**: Links a quality assessment process to the indicator it evaluates.
-**findsSource**: Links a quality assessment process to the data source identified in the knowledge graph.
-**hasGranularity**: Specifies the granularity level at which DQ is assessed.
-**hasLabel**: Links a quality measurement to an assessment label.
-**producesResult**: Associates a quality assessment process with its resulting evaluation.
-**usesMethod**: Links a quality assessment process to the specific method used.
-**usesMetric**: Defines the metric used in a formula-based method.
### Data Properties
The ontology defines the following data properties:
-**labelThreshold**: Specifies the numerical threshold for assigning a quality label (e.g., "High Quality" threshold = 80%).
## Usage
### Use Case
We consider a simple baseline example. The use case involves five devices installed in a classroom within a campus building. These devices include five sensors: two presence sensors and three integrated into three platforms—an air conditioner, a projector, and a ventilation system—all of which track their energy consumption (in watts) as observations. The use case is described using SOSA.
<palign="center">
<imgsrc="usecase.png"alt="Use case Overview">
</p>
### Steps of DQA based approach and examples
#### Step 1: Enriching the Observation
The first step involves receiving an observation and enriching it using the **SOSA** ontology. This phase does not require describing the processing of the observation in terms of representational indicators, as these mainly concern the proper representation of data rather than its qualitative assessment. Once enrichment is completed, the observation is stored in the GC repository to be used in the evaluation phases.
```ttl
ex:obs_001 a sosa:Observation ;
rdfs:comment "Observation of energy consumption from airconditioner#32" ;
-[ ] [Set up project integrations](https://gitlab.irit.fr/projet-ai-nrgy/dqa-ontology/-/settings/integrations)
#### Step 2: Evaluating DQ Indicators at the UO Level
At this step, DQ indicators that can be assessed on a single observation (**UO**) are applied. These include **accuracy**, **provenance**, **relevance**, and **timeliness**. Each indicator is evaluated individually, and the resulting data is stored in the repository with a complete description of the evaluation process using the **SOSA**, **DQV**, and **DQA** ontologies. The **DQV** ontology is used to describe the evaluation of DQ dimensions (indicators), while **DQA** extends this descriptive model by integrating metadata associated with the evaluation process.
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##### Formula-Based Method (Accuracy) :
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ex:Accuracy a dqv:Dimension ;
skos:definition "The considered data value must be close to the realistic expected value, ..." ;
skos:prefLabel "Accuracy"@en .
***
ex:Accuracy_obs_018Result a dqv:QualityMeasurement ;
When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
## Suggestions for a good README
Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
##### Source-Based Method (Provenance) :
## Name
Choose a self-explaining name for your project.
```ttl
ex:ProvenanceAssessmentobs_018 a dqa:QualityAssessmentProcess ;
Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
ex:ProvenanceMethod a dqa:SourceBasedMethod ;
dqv:inDimension ex:Provenance .
## Badges
On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
ex:Provenance a dqv:Dimension ;
skos:definition "Provenance refers to the source of the data values and its description." ;
skos:prefLabel "Provenance"@en .
```
## Visuals
Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
## Installation
Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
#### Step 3: Evaluating DQ Indicators at the SO-SD and SO-MD Levels
## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
In this final step, the evaluation focuses on sets of observations (**SO-SD** and **SO-MD**). Two types of evaluations can be performed:
-**Task-Agnostic Evaluation**: Some DQ indicators can be evaluated independently of a user-defined objective, only considering the context of the observations’ generation. These indicators include **precision**, **duplication**, and **consistency**.
-**Task-Specific Evaluation**: When a user defines a specific task, additional DQ indicators may be necessary, alongside the three previously mentioned indicators. These include **completeness**, **timeliness**, and **relevance**.
To structure this step, the **OBOE** ontology is used to define the sets of observations on which the evaluation is performed. Then, the evaluation results are documented using **DQV**, and the evaluation process is described using **DQA**.
Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
## Download
## Roadmap
If you have ideas for releases in the future, it is a good idea to list them in the README.
To use the DQA ontology in your project, **Download or Clone the Repository**: You can download the RDF file containing the DQA ontology or clone the repository from GitHub.
## Contributing
State if you are open to contributions and what your requirements are for accepting them.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
## Contributors
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
- Oumaima Amal - [LinkedIn](https://www.linkedin.com/in/oumaima-amal/)
Show your appreciation to those who have contributed to the project.
## Contact
## License
For open source projects, say how it is licensed.
For further questions or support, please contact Oumaima Amal via email: oumaima.amal@irit.fr.
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
