In recent years, large language models (LLMs) have garnered increasing attention from both
academia and industry due to their potential to facilitate natural language processing (NLP) …

We present Nemo, a toolkit for rule-based reasoning and data processing that emphasises
robustness and ease of use. Nemo's core is a scalable and efficient main-memory reasoner …

Why-provenance—explaining why a query result is obtained—is an essential asset for
reaching the goal of Explainable AI. For instance, recursive (Datalog) queries may show …

Existential rules are a very popular ontology-mediated query language for which the chase
represents a generic computational approach for query answering. It is straightforward that …

To reason with existential rules (aka tuple-generating dependencies), one often computes
universal models. Among the many such models of different structure and cardinality, the …

Especially in data-intensive settings, a promising reasoning approach for description logics
(DLs) is to rewrite DL theories into sets of rules. Although many such approaches have been …

We study the extension of non-monotonic disjunctive logic programs with terms that
represent sets of constants, called DLP (S), under the stable model semantics. This strictly …

The chase is a widely implemented approach to reason with tuple-generating dependencies
(tgds), used in data exchange, data integration, and ontology-based query answering …

Datalog is a popular logic programming language for deductive reasoning tasks in a wide
array of applications, including business analytics, program analysis, and ontological …

Several types of dependencies have been proposed for the static analysis of existential rule
ontologies, promising insights about computational properties and possible practical uses of …