Attribution scores can be applied in data management to quantify the contribution of
individual items to conclusions from the data, as part of the explanation of what led to these …

Quantifying the inconsistency of a database is motivated by various goals including
reliability estimation for new datasets and progress indication in data cleaning. Another goal …

Real-world databases are often inconsistent. Although there has been an extensive body of
work on handling inconsistency, little work has been done on measuring inconsistency in …

Real-world data are often inconsistent. Although a substantial amount of research has been
done on measuring inconsistency, this research concentrated on knowledge bases …

Handling conflicting information is an important challenge in AI. Measuring inconsistency is
an approach that provides ways to quantify the severity of inconsistency and helps …

Denial constraints (DCs) are an integrity constraint formalism widely used to detect
inconsistencies in data. Several algorithms have been devised to discover DCs from data …

As a key step of data preparation, it is always necessary to first assert the quality of data
before conducting any data application. Given a set of constraints, the validity measure …

AI systems often need to deal with inconsistencies. One way of getting information about
inconsistencies is by measuring the amount of information in the knowledgebase. In the past …

Conflicts are inherent to normative systems. In this paper, we explore a novel approach to
normative reasoning by quantifying the amount of conflicts within normative systems. We …

Logical conflicts are likely to arise in logic-based intelligent systems. Managing these
conflicts has been intensely studied in various parts of Artificial Intelligence (AI). So far, the …