24 citations · 37 across the 11 of their papers we have counts for
13 papers
Optimized Crystallographic Graph Generation for Material Science
Astrid Klipfel, Yaël Frégier, Adlane Sayede +1
Graph neural networks are widely used in machine learning applied to chemistry, and in particular for material science discovery. For crystalline materials, however, generating gra…
Unified Model for Crystalline Material Generation
Astrid Klipfel, Yaël Frégier, Adlane Sayede +1
One of the greatest challenges facing our society is the discovery of new innovative crystal materials with specific properties. Recently, the problem of generating crystal materia…
Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy
Na Li, Zied Bouraoui, Steven Schockaert
Ultra-fine entity typing (UFET) is the task of inferring the semantic types, from a large set of fine-grained candidates, that apply to a given entity mention. This task is especia…
Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Models
Na Li, Hanane Kteich, Zied Bouraoui +1
Learning vectors that capture the meaning of concepts remains a fundamental challenge. Somewhat surprisingly, perhaps, pre-trained language models have thus far only enabled modest…
Equivariant Message Passing Neural Network for Crystal Material Discovery
Astrid Klipfel, Olivier Peltre, Najwa Harrati +3
Automatic material discovery with desired properties is a fundamental challenge for material sciences. Considerable attention has recently been devoted to generating stable crystal…
Region-Based Merging of Open-Domain Terminological Knowledge
Zied Bouraoui, Sebastien Konieczny, Thanh Ma +2
This paper introduces a novel method for merging open-domain terminological knowledge. It takes advantage of the Region Connection Calculus (RCC5), a formalism used to represent re…
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…
Modelling Semantic Categories using Conceptual Neighborhood
Zied Bouraoui, Jose Camacho-Collados, Luis Espinosa-Anke +1
While many methods for learning vector space embeddings have been proposed in the field of Natural Language Processing, these methods typically do not distinguish between categorie…
Inducing Relational Knowledge from BERT
Zied Bouraoui, Jose Camacho-Collados, Steven Schockaert
One of the most remarkable properties of word embeddings is the fact that they capture certain types of semantic and syntactic relationships. Recently, pre-trained language models…
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…
Modeling Semantic Relatedness using Global Relation Vectors
Shoaib Jameel, Zied Bouraoui, Steven Schockaert
Word embedding models such as GloVe rely on co-occurrence statistics from a large corpus to learn vector representations of word meaning. These vectors have proven to capture surpr…
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…
A General Modifier-based Framework for Inconsistency-Tolerant Query Answering
Jean Francois Baget, Salem Benferhat, Zied Bouraoui +5
We propose a general framework for inconsistency-tolerant query answering within existential rule setting. This framework unifies the main semantics proposed by the state of art an…