We investigate a number of semantically defined fragments of Tarski's al...
Graph Neural Networks (GNNs) are a form of deep learning that enable a w...
SHACL is a W3C-proposed schema language for expressing structural constr...
The logic of information flows (LIF) has recently been proposed as a gen...
The logic of information flows (LIF) is a general framework in which tas...
Motivated by old and new applications, we investigate Datalog as a langu...
In the last decade, the term instance-spanning constraint has been intro...
Temporal graphs represent graph evolution over time, and have been recei...
We investigate the power of message-passing neural networks (MPNNs) in t...
In constraint languages for RDF graphs, such as ShEx and SHACL, constrai...
SHACL is a W3C-proposed language for expressing structural constraints o...
We propose a logical framework, based on Datalog, to study the foundatio...
We introduce a novel variant of BSS machines called Separate Branching B...
We show that the matrix query language MATLANG corresponds to a
natural ...
We propose a logical characterization of problems solvable in determinis...
When a relational database is queried, the result is normally a relation...
Second-order transitive-closure logic, SO(TC), is an expressive declarat...
Motivated by the continuing interest in the tree data model, we study th...
We investigate the expressive power of MATLANG, a formal language
for ma...
We consider a reinforcement learning framework where agents have to navi...
Conjunctive database queries have been extended with a mechanism for obj...
We study the expressive power of positive neural networks. The model use...
The satisfiability problem for SPARQL patterns is undecidable in general...
Cause-effect relations are an important part of human knowledge. In real...
In recent years, the problem of association rule mining in transactional...