This work primarily aims to propose a generic and reusable approach for data
quality evaluation, which is a key aspect of data management. To this end, we
extend the DQV ontology by introducing additional concepts that allow for a
more structured and traceable management of quality assessments. To better
clarify the objectives and expected capabilities of the proposed DQA extension,
we define the following competency questions:
- Which quality assessment processes have been performed on a specific ob-
servation or a set of observations ?
- What assessment method (formula-based or source-based) has been applied
in a given quality assessment process?
- What is the granularity level (Unique Observation or Set of Observations)
associated with a specific assessment process ?
- Which specific quality indicator (e.g., accuracy, timeliness, completeness)
was assessed by a given process ?
- What is the measured value and the assigned label resulting from an assess-
ment process ?
- Which data sources were identified or retrieved by source-based assessment
methods ?
### Overview
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.