Representing Dataset Quality Metadata using Multi-Dimensional Views

TL;DR

This paper introduces the Dataset Quality Ontology (daQ), a framework for representing and visualizing data quality metadata as multi-dimensional statistical observations to aid data consumers and publishers.

Contribution

The paper presents daQ, a core vocabulary for encoding dataset quality metrics as multi-dimensional data cube observations, enabling better analysis and visualization of data quality.

Findings

daQ supports embedding quality metadata into linked datasets

It facilitates analysis of data versions and quality browsing

Data cube visualization tools enhance quality assessment

Abstract

Data quality is commonly defined as fitness for use. The problem of identifying quality of data is faced by many data consumers. Data publishers often do not have the means to identify quality problems in their data. To make the task for both stakeholders easier, we have developed the Dataset Quality Ontology (daQ). daQ is a core vocabulary for representing the results of quality benchmarking of a linked dataset. It represents quality metadata as multi-dimensional and statistical observations using the Data Cube vocabulary. Quality metadata are organised as a self-contained graph, which can, e.g., be embedded into linked open datasets. We discuss the design considerations, give examples for extending daQ by custom quality metrics, and present use cases such as analysing data versions, browsing datasets by quality, and link identification. We finally discuss how data cube visualisation tools enable data publishers and consumers to analyse better the quality of their data.