Steven Schockaert

research

∙ 08/14/2023

Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis

The task of inductive knowledge graph completion requires models to lear...

0 Akash Anil, et al. ∙

research

∙ 05/24/2023

A RelEntLess Benchmark for Modelling Graded Relations between Named Entities

Relations such as "is influenced by", "is known for" or "is a competitor...

0 Asahi Ushio, et al. ∙

research

∙ 05/22/2023

EnCore: Pre-Training Entity Encoders using Coreference Chains

Entity typing is the task of assigning semantic types to the entities th...

0 Frank Mtumbuka, et al. ∙

research

∙ 05/22/2023

Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy

Ultra-fine entity typing (UFET) is the task of inferring the semantic ty...

0 Na Li, et al. ∙

research

∙ 05/16/2023

Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Models

Learning vectors that capture the meaning of concepts remains a fundamen...

0 Na Li, et al. ∙

research

∙ 05/15/2023

What's the Meaning of Superhuman Performance in Today's NLU?

In the last five years, there has been a significant focus in Natural La...

0 Simone Tedeschi, et al. ∙

research

∙ 10/11/2022

Embeddings as Epistemic States: Limitations on the Use of Pooling Operators for Accumulating Knowledge

Various neural network architectures rely on pooling operators to aggreg...

0 Steven Schockaert, et al. ∙

research

∙ 10/06/2022

Modelling Commonsense Properties using Pre-Trained Bi-Encoders

Grasping the commonsense properties of everyday concepts is an important...

5 Amit Gajbhiye, et al. ∙

research

∙ 06/28/2021

Modelling Monotonic and Non-Monotonic Attribute Dependencies with Embeddings: A Theoretical Analysis

During the last decade, entity embeddings have become ubiquitous in Arti...

0 Steven Schockaert, et al. ∙

research

∙ 06/15/2021

Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection

One of the long-standing challenges in lexical semantics consists in lea...

0 Yixiao Wang, et al. ∙

research

∙ 06/14/2021

Probing Pre-Trained Language Models for Disease Knowledge

Pre-trained language models such as ClinicalBERT have achieved impressiv...

0 Israa Alghanmi, et al. ∙

research

∙ 05/11/2021

BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?

Analogies play a central role in human commonsense reasoning. The abilit...

0 Asahi Ushio, et al. ∙

research

∙ 05/10/2021

A Description Logic for Analogical Reasoning

Ontologies formalise how the concepts from a given domain are interrelat...

0 Steven Schockaert, et al. ∙

research

∙ 12/04/2020

Modelling General Properties of Nouns by Selectively Averaging Contextualised Embeddings

While the success of pre-trained language models has largely eliminated ...

0 Na Li, et al. ∙

research

∙ 11/16/2020

Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities

In this paper, we introduce a new annotated dataset which is aimed at su...

0 Carla Pérez-Almendros, et al. ∙

research

∙ 06/25/2020

Plausible Reasoning about EL-Ontologies using Concept Interpolation

Description logics (DLs) are standard knowledge representation languages...

0 Yazmin Ibáñez-García, et al. ∙

research

∙ 12/13/2019

From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)

This paper proposes a tentative and original survey of meeting points be...

17 Zied Bouraoui, et al. ∙

research

∙ 12/03/2019

Modelling Semantic Categories using Conceptual Neighborhood

While many methods for learning vector space embeddings have been propos...

0 Zied Bouraoui, et al. ∙

research

∙ 11/28/2019

Inducing Relational Knowledge from BERT

One of the most remarkable properties of word embeddings is the fact tha...

0 Zied Bouraoui, et al. ∙

research

∙ 10/16/2019

Meemi: A Simple Method for Post-processing Cross-lingual Word Embeddings

Word embeddings have become a standard resource in the toolset of any Na...

0 Yerai Doval, et al. ∙

research

∙ 10/16/2019

Meemi: Finding the Middle Ground in Cross-lingual Word Embeddings

Word embeddings have become a standard resource in the toolset of any Na...

0 Yerai Doval, et al. ∙

research

∙ 09/13/2019

Learning Household Task Knowledge from WikiHow Descriptions

Commonsense procedural knowledge is important for AI agents and robots t...

0 Yilun Zhou, et al. ∙

research

∙ 08/21/2019

On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning

Cross-lingual word embeddings are vector representations of words in dif...

0 Yerai Doval, et al. ∙

research

∙ 06/04/2019

Relational Word Embeddings

While word embeddings have been shown to implicitly encode various forms...

0 Jose Camacho-Collados, et al. ∙

research

∙ 05/17/2019

Learning Cross-lingual Embeddings from Twitter via Distant Supervision

Cross-lingual embeddings represent the meaning of words from different l...

0 Jose Camacho-Collados, et al. ∙

research

∙ 02/21/2019

Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness

In many applications, it is important to characterize the way in which t...

0 Yilun Zhou, et al. ∙

research

∙ 10/12/2018

Embedding Geographic Locations for Modelling the Natural Environment using Flickr Tags and Structured Data

Meta-data from photo-sharing websites such as Flickr can be used to obta...

0 Shelan S. Jeawak, et al. ∙

research

∙ 08/27/2018

Improving Cross-Lingual Word Embeddings by Meeting in the Middle

Cross-lingual word embeddings are becoming increasingly important in mul...

0 Yerai Doval, et al. ∙

research

∙ 08/18/2018

SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors

We present SeVeN (Semantic Vector Networks), a hybrid resource that enco...

0 Luis Espinosa-Anke, et al. ∙

research

∙ 05/26/2018

From Knowledge Graph Embedding to Ontology Embedding: Region Based Representations of Relational Structures

Recent years have witnessed the enormous success of low-dimensional vect...

0 Víctor Gutiérrez-Basulto, et al. ∙

research

∙ 05/03/2018

Learning Conceptual Space Representations of Interrelated Concepts

Several recently proposed methods aim to learn conceptual space represen...

0 Zied Bouraoui, et al. ∙

research

∙ 04/17/2018

VC-Dimension Based Generalization Bounds for Relational Learning

In many applications of relational learning, the available data can be s...

0 Ondrej Kuzelka, et al. ∙

research

∙ 03/15/2018

PAC-Reasoning in Relational Domains

We consider the problem of predicting plausible missing facts in relatio...

0 Ondrej Kuzelka, et al. ∙

research

∙ 11/14/2017

Modeling Semantic Relatedness using Global Relation Vectors

Word embedding models such as GloVe rely on co-occurrence statistics fro...

0 Shoaib Jameel, et al. ∙

research

∙ 10/05/2017

Stacked Structure Learning for Lifted Relational Neural Networks

Lifted Relational Neural Networks (LRNNs) describe relational domains us...

0 Gustav Sourek, et al. ∙

research

∙ 09/18/2017

Relational Marginal Problems: Theory and Estimation

In the propositional setting, the marginal problem is to find a (maximum...

0 Ondrej Kuzelka, et al. ∙

research

∙ 08/21/2017

Probabilistic Relation Induction in Vector Space Embeddings

Word embeddings have been found to capture a surprisingly rich amount of...

0 Zied Bouraoui, et al. ∙

research

∙ 06/21/2017

Jointly Learning Word Embeddings and Latent Topics

Word embedding models such as Skip-gram learn a vector-space representat...

0 Bei Shi, et al. ∙

research

∙ 05/19/2017

Induction of Interpretable Possibilistic Logic Theories from Relational Data

The field of Statistical Relational Learning (SRL) is concerned with lea...

0 Ondrej Kuzelka, et al. ∙

research

∙ 11/18/2016

Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)

In this paper, we advocate the use of stratified logical theories for re...

0 Ondrej Kuzelka, et al. ∙

research

∙ 02/18/2016

Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning

Conceptual spaces are geometric representations of conceptual knowledge,...

0 Shoaib Jameel, et al. ∙

research

∙ 12/16/2015

Solving stable matching problems using answer set programming

Since the introduction of the stable marriage problem (SMP) by Gale and ...

0 Sofie De Clercq, et al. ∙

research

∙ 06/03/2015

Encoding Markov Logic Networks in Possibilistic Logic

Markov logic uses weighted formulas to compactly encode a probability di...

0 Ondrej Kuzelka, et al. ∙

research

∙ 10/09/2014

Realizing RCC8 networks using convex regions

RCC8 is a popular fragment of the region connection calculus, in which q...

0 Steven Schockaert, et al. ∙

research

∙ 11/30/2013

Characterizing and Extending Answer Set Semantics using Possibility Theory

Answer Set Programming (ASP) is a popular framework for modeling combina...

0 Kim Bauters, et al. ∙

research

∙ 02/28/2013

Modeling Stable Matching Problems with Answer Set Programming

The Stable Marriage Problem (SMP) is a well-known matching problem first...

0 Sofie De Clercq, et al. ∙

research

∙ 03/15/2012

Possibilistic Answer Set Programming Revisited

Possibilistic answer set programming (PASP) extends answer set programmi...

0 Kim Bauters, et al. ∙

Steven Schockaert

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