77 citations · 353 across the 60 of their papers we have counts for
25 papers · 1 filter
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…
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…
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…
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…
Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis
Akash Anil, Víctor Gutiérrez-Basulto, Yazmín Ibañéz-García +1
The task of inductive knowledge graph completion requires models to learn inference patterns from a training graph, which can then be used to make predictions on a disjoint test gr…
Artificial virtuous agents in a multiagent tragedy of the commons
Jakob Stenseke
Although virtue ethics has repeatedly been proposed as a suitable framework for the development of artificial moral agents (AMAs), it has been proven difficult to approach from a c…
Embeddings as Epistemic States: Limitations on the Use of Pooling Operators for Accumulating Knowledge
Steven Schockaert
Various neural network architectures rely on pooling operators to aggregate information coming from different sources. It is often implicitly assumed in such contexts that vectors…
Modelling Monotonic and Non-Monotonic Attribute Dependencies with Embeddings: A Theoretical Analysis
Steven Schockaert
During the last decade, entity embeddings have become ubiquitous in Artificial Intelligence. Such embeddings essentially serve as compact but semantically meaningful representation…
A Description Logic for Analogical Reasoning
Steven Schockaert, Yazmín Ibáñez-García, Víctor Gutiérrez-Basulto
Ontologies formalise how the concepts from a given domain are interrelated. Despite their clear potential as a backbone for explainable AI, existing ontologies tend to be highly in…
Plausible Reasoning about EL-Ontologies using Concept Interpolation
Yazmín Ibáñez-García, Víctor Gutiérrez-Basulto, Steven Schockaert
Description logics (DLs) are standard knowledge representation languages for modelling ontologies, i.e. knowledge about concepts and the relations between them. Unfortunately, DL o…
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
Zied Bouraoui, Antoine Cornuéjols, Thierry Denœux +9
This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been deve…
Learning Conceptual Space Representations of Interrelated Concepts
Zied Bouraoui, Steven Schockaert
Several recently proposed methods aim to learn conceptual space representations from large text collections. These learned representations asso- ciate each object from a given doma…
From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules
Víctor Gutiérrez-Basulto, Steven Schockaert
Recent years have witnessed the successful application of low-dimensional vector space representations of knowledge graphs to predict missing facts or find erroneous ones. However,…
PAC-Reasoning in Relational Domains
Ondrej Kuzelka, Yuyi Wang, Jesse Davis +1
We consider the problem of predicting plausible missing facts in relational data, given a set of imperfect logical rules. In particular, our aim is to provide bounds on the (expect…
Relational Marginal Problems: Theory and Estimation
Ondrej Kuzelka, Yuyi Wang, Jesse Davis +1
In the propositional setting, the marginal problem is to find a (maximum-entropy) distribution that has some given marginals. We study this problem in a relational setting and make…
Probabilistic Relation Induction in Vector Space Embeddings
Zied Bouraoui, Shoaib Jameel, Steven Schockaert
Word embeddings have been found to capture a surprisingly rich amount of syntactic and semantic knowledge. However, it is not yet sufficiently well-understood how the relational kn…
Induction of Interpretable Possibilistic Logic Theories from Relational Data
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kin…
Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
In this paper, we advocate the use of stratified logical theories for representing probabilistic models. We argue that such encodings can be more interpretable than those obtained…
Learning Possibilistic Logic Theories from Default Rules
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
We introduce a setting for learning possibilistic logic theories from defaults of the form "if alpha then typically beta". We first analyse this problem from the point of view of m…
Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning
Shoaib Jameel, Steven Schockaert
Conceptual spaces are geometric representations of conceptual knowledge, in which entities correspond to points, natural properties correspond to convex regions, and the dimensions…
Encoding Markov Logic Networks in Possibilistic Logic
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be di…
Realizing RCC8 networks using convex regions
Steven Schockaert, Sanjiang Li
RCC8 is a popular fragment of the region connection calculus, in which qualitative spatial relations between regions, such as adjacency, overlap and parthood, can be expressed. Whi…
Modeling Stable Matching Problems with Answer Set Programming
Sofie De Clercq, Steven Schockaert, Martine De Cock +1
The Stable Marriage Problem (SMP) is a well-known matching problem first introduced and solved by Gale and Shapley (1962). Several variants and extensions to this problem have sinc…