5 papers · 1 filter
Faithful Differentiable Reasoning with Reshuffled Region-based Embeddings
Aleksandar Pavlovic, Emanuel Sallinger, Steven Schockaert
Knowledge graph (KG) embedding methods learn geometric representations of entities and relations to predict plausible missing knowledge. These representations are typically assumed…
Large Language and Reasoning Models are Shallow Disjunctive Reasoners
Irtaza Khalid, Amir Masoud Nourollah, Steven Schockaert
Large Language Models (LLMs) have been found to struggle with systematic reasoning. Even on tasks where they appear to perform well, their performance often depends on shortcuts, r…
Systematic Relational Reasoning With Epistemic Graph Neural Networks
Irtaza Khalid, Steven Schockaert
Developing models that can learn to reason is a notoriously challenging problem. We focus on reasoning in relational domains, where the use of Graph Neural Networks (GNNs) seems li…
Capturing Knowledge Graphs and Rules with Octagon Embeddings
Victor Charpenay, Steven Schockaert
Region based knowledge graph embeddings represent relations as geometric regions. This has the advantage that the rules which are captured by the model are made explicit, making it…
Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings
Hanane Kteich, Na Li, Usashi Chatterjee +2
Concept embeddings offer a practical and efficient mechanism for injecting commonsense knowledge into downstream tasks. Their core purpose is often not to predict the commonsense p…