77 citations · 206 across the 38 of their papers we have counts for
7 papers · 1 filter
Modeling Multi-modal Cross-interaction for Multi-label Few-shot Image Classification Based on Local Feature Selection
Kun Yan, Zied Bouraoui, Fangyun Wei +4
The aim of multi-label few-shot image classification (ML-FSIC) is to assign semantic labels to images, in settings where only a small number of training examples are available for…
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…
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…
Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis
Na Li, Thomas Bailleux, Zied Bouraoui +1
We consider the problem of finding plausible knowledge that is missing from a given ontology, as a generalisation of the well-studied taxonomy expansion task. One line of work trea…
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…
Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies
Nitesh Kumar, Usashi Chatterjee, Steven Schockaert
Conceptual spaces represent entities in terms of their primitive semantic features. Such representations are highly valuable but they are notoriously difficult to learn, especially…