Ontology-Based Schema-Level Data Quality: The Case of Consistency

The quality of metadata plays a crucial role in many data FAIRification processes. So much so, in fact, that all the four main principles of data FAIRification prescribe the use of high-quality metadata. One of the main data management paradigms where metadata is a first-class citizen is Ontology-Based Data Management (OBDM). The goal of OBDM is to provide users with a reconciled view of a set of heterogeneous data sources by means of a semantic metadata layer comprising an ontology and a mapping. The former is a high-level, declarative representation of the domain of interest written in terms of a logical theory, and the latter is a formal description of the relation between the symbols in the ontology and the data at the sources. In this article, we introduce a novel data quality framework based on OBDM and specifically tailored for metadata analysis. The target of this framework is one of the most common forms of metadata currently in circulation, i.e., the integrity constraints defined by a database schema. Specifically, we will focus on the data quality dimension known as Consistency, i.e., the property of data that is free of contradictions and incoherence. In this context, our techniques provide a set of tools to compare the integrity constraints defined by a database schema against the knowledge encoded in an ontology and check whether these constraints are strict enough (i.e., protect) and are not too strict (i.e., are faithful to) for such knowledge. The contribution of the article is the presentation of the framework and the study of the related computational problems. We will present a detailed computational complexity analysis of such problems and show that they are decidable for classes of OBDM specifications and integrity constraints that are very popular in practice.