Existential rules, a.k.a. tuple-generating dependencies (TGDs), form a well-established formalism for specifying ontologies. In particular, the warded language is a well-behaved fragment of TGD-based ontologies, striking a good balance between expressive power and computational complexity of answering Ontology-Mediated Queries (OMQs). The theoretical foundations of answering OMQs over warded ontologies are by now well-understood, but to the best of our knowledge, very few efforts exist that exploit such a rich theory for building practical query answering algorithms. Our goal is to fill the above gap by designing a novel Datalog rewriting algorithm for OMQs over warded ontologies which is amenable to practical implementations, as well as providing an implementation and an experimental evaluation, with the aim of understanding how key input parameters affect the performance of this approach, and what are its limits when combined with off-the-shelf Datalog-based engines.

Over the past years, there has been a growing demand for ontological reasoning systems based on languages of the Datalog+/- family, such as Vadalog, for their ability to effectively model a wide range of real-world problems with powerful features such as existential quantification. As the scale and complexity of data analysis tasks continue to grow, the ability to distribute the computational workload across multiple non-communicating processors has become vital for these systems to achieve scalable performance. The joint presence of existential quantification and recursion poses new challenges, currently unsolved by existing distributed systems, which only concentrate on Datalog and are therefore unsuitable for ontological reasoning. When working across multiple processors, generating all the facts to answer a specific reasoning query, avoiding duplication, and guaranteeing termination are non-trivial tasks as infinitely many new symbols and facts can be generated by existential quantification and recursion. In this paper, we address such challenges and introduce the first distributed framework in the Datalog+/- space. We propose the condition of homomorphic decomposability, which identifies sets of Datalog+/- rules with good distribution properties. We put homomorphic decomposability into action with a distributed reasoning algorithm for Warded Datalog+/-, the core of Vadalog. We implement Vadalog Parallel, a distributed reasoner for Vadalog and provide experimental evaluation against state-of-the-art systems.

Motivated by our experience in building the Enterprise Knowledge Graph of Italian companies for the Central Bank of Italy, in this paper we present an in-depth case analysis of company ownership graphs, graphs having company ownership as a central concept. In particular, we study and introduce three industrially relevant problems related to such graphs: company control, asset eligibility and detection of personal links. We formally characterize the problems and present Vada-Link, a framework based on state-of-the-art approaches for knowledge representation and reasoning. With our methodology and system, we solve the problems at hand in a scalable, model-independent and generalizable way. We illustrate the favourable architectural properties of Vada-Link and give experimental evaluation of the approach.