33 citations · 143 across the 28 of their papers we have counts for
31 papers
On the generic information capacity of relational schemas with a single binary relation
Benoît Groz, Jan Hidders, Nina Pardal +2
We consider database schemas consisting of a single binary relation, with key constraints and inclusion dependencies. Over this space of 20 schemas, we completely characterize when…
A Logical View of GNN-Style Computation and the Role of Activation Functions
Pablo Barceló, Floris Geerts, Matthias Lanzinger +2
We study the numerical and Boolean expressiveness of MPLang, a declarative language that captures the computation of graph neural networks (GNNs) through linear message passing and…
Halting Recurrent GNNs and the Graded -Calculus
Jeroen Bollen, Jan Van den Bussche, Stijn Vansummeren +1
Graph Neural Networks (GNNs) are a class of machine-learning models that operate on graph-structured data. Their expressive power is intimately related to logics that are invariant…
Query languages for neural networks
Martin Grohe, Christoph Standke, Juno Steegmans +1
We lay the foundations for a database-inspired approach to interpreting and understanding neural network models by querying them using declarative languages. Towards this end we st…
Expressiveness of SHACL Features and Extensions for Full Equality and Disjointness Tests
Bart Bogaerts, Maxime Jakubowski, Jan Van den Bussche
SHACL is a W3C-proposed schema language for expressing structural constraints on RDF graphs. Recent work on formalizing this language has revealed a striking relationship to descri…
Executable First-Order Queries in the Logic of Information Flows
Heba Aamer, Bart Bogaerts, Dimitri Surinx +2
The logic of information flows (LIF) has recently been proposed as a general framework in the field of knowledge representation. In this framework, tasks of procedural nature can s…