6 citations · 18 across the 13 of their papers we have counts for
14 papers
Neuro-Relational Programs: Unifying Queries and Neural Computation over Structured Data
Arie Soeteman, Balder ten Cate, Maurice Funk +3
The conventional approach to deep learning over relational databases applies neural models, such as Graph Neural Networks (GNNs), to a graph representation of the database. Recent…
Expressive Power of Deep Homomorphism Networks over Relational Databases
Moritz Schönherr, Balder ten Cate, Maurice Funk +3
The expressive limitations of message-passing Graph Neural Networks (GNNs) have motivated a wide range of more powerful graph learning architectures. We advocate Deep Homomorphism…
Bounded Fitting for Expressive Description Logics
Maurice Funk, Jean Christoph Jung, Tom Voellmer
Bounded fitting is an attractive paradigm for learning logical formulas from labeled data examples that offers PAC-style generalization guarantees and can often be implemented leve…
Towards Understanding the Expressive Power of GNNs with Global Readout
Maurice Funk, Daumantas Kojelis
We study the expressive power of message-passing aggregate-combine-readout graph neural networks (ACR-GNNs). Particularly, we focus on the first-order (FO) properties expressible b…
Fitting Description Logic Ontologies to ABox and Query Examples
Maurice Funk, Marvin Grosser, Carsten Lutz
We study a fitting problem inspired by ontology-mediated querying: given a collection of positive and negative examples of the form with an ABox and…
Expressive Power of Graph Transformers via Logic
Veeti Ahvonen, Maurice Funk, Damian Heiman +2
Transformers are the basis of modern large language models, but relatively little is known about their precise expressive power on graphs. We study the expressive power of graph tr…
SAT-Based Bounded Fitting for the Description Logic ALC
Maurice Funk, Jean Christoph Jung, Tom Voellmer
Bounded fitting is a general paradigm for learning logical formulas from positive and negative data examples, that has received considerable interest recently. We investigate bound…
Extremal Fitting CQs do not Generalize
Balder ten Cate, Maurice Funk, Jean Christoph Jung +1
A fitting algorithm for conjunctive queries (CQs) produces, given a set of positively and negatively labeled data examples, a CQ that fits these examples. In general, there may be…
Towards Ontology Construction with Language Models
Maurice Funk, Simon Hosemann, Jean Christoph Jung +1
We present a method for automatically constructing a concept hierarchy for a given domain by querying a large language model. We apply this method to various domains using OpenAI's…
SAT-Based PAC Learning of Description Logic Concepts
Balder ten Cate, Maurice Funk, Jean Christoph Jung +1
We propose bounded fitting as a scheme for learning description logic concepts in the presence of ontologies. A main advantage is that the resulting learning algorithms come with t…
On the non-efficient PAC learnability of conjunctive queries
Balder ten Cate, Maurice Funk, Jean Christoph Jung +1
This note serves three purposes: (i) we provide a self-contained exposition of the fact that conjunctive queries are not efficiently learnable in the Probably-Approximately-Correct…
Extremal Fitting Problems for Conjunctive Queries
Balder ten Cate, Victor Dalmau, Maurice Funk +1
The fitting problem for conjunctive queries (CQs) is the problem to construct a CQ that fits a given set of labeled data examples. When a fitting CQ exists, it is in general not un…
Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologies
Maurice Funk, Jean Christoph Jung, Carsten Lutz
We study ELI queries (ELIQs) in the presence of ontologies formulated in the description logic DL-Lite. For the dialect DL-LiteH, we show that ELIQs have a frontier (set of least g…
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies
Maurice Funk, Jean Christoph Jung, Carsten Lutz
We consider the problem to learn a concept or a query in the presence of an ontology formulated in the description logic ELr, in Angluin's framework of active learning that allows…