7 citations · 30 across the 12 of their papers we have counts for
11 papers · 1 filter
NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning
Daniel Romero Schellhorn, Till Mossakowski, Björn Gehrke
Neurosymbolic semantics is fragmented: classical, fuzzy, probabilistic and neural systems each define truth by their own inductive rules. NeSyCat, extending ULLER, subsumes them un…
NeSyCat: A Monad-Based Categorical Semantics of the Neurosymbolic ULLER Framework
Daniel Romero Schellhorn, Till Mossakowski
ULLER (Unified Language for LEarning and Reasoning) offers a unified first-order logic (FOL) syntax, enabling its knowledge bases to be used directly across a wide range of neurosy…
The Possibility of Artificial Intelligence Becoming a Subject and the Alignment Problem
Till Mossakowski, Helena Esther Grass
The prospect of Artificial General Intelligence (AGI) is increasingly driving institutional decisions, and alignment of AGI is a hard problem. The currently dominant AI alignment s…
A fuzzy loss for ontology classification
Simon Flügel, Martin Glauer, Till Mossakowski +1
Deep learning models are often unaware of the inherent constraints of the task they are applied to. However, many downstream tasks require logical consistency. For ontology classif…
Automated and Explainable Ontology Extension Based on Deep Learning: A Case Study in the Chemical Domain
Adel Memariani, Martin Glauer, Fabian Neuhaus +2
Reference ontologies provide a shared vocabulary and knowledge resource for their domain. Manual construction enables them to maintain a high quality, allowing them to be widely ac…
Generic Ontology Design Patterns: Roles and Change over Time
Bernd Krieg-Brückner, Till Mossakowski, Mihai Codescu
In this chapter we propose Generic Ontology Design Patterns, GODPs, as a methodology for representing and instantiating ontology design patterns in a way that is adaptable, and all…