no code implementations • 26 Aug 2024 • Pierre Monnin, Cherif-Hassan Nousradine, Lucas Jarnac, Laurel Zuckerman, Miguel Couceiro
Knowledge graphs (KGs) have become ubiquitous publicly available knowledge sources, and are nowadays covering an ever increasing array of domains.
no code implementations • 8 Dec 2023 • Nicolas Hubert, Pierre Monnin, Heiko Paulheim
Consequently, a larger body of works focuses on the completion of missing information in KGs, which is commonly referred to as link prediction (LP).
no code implementations • 29 Sep 2023 • Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, Valentina Tamma
The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines.
1 code implementation • 7 Sep 2023 • Nicolas Hubert, Pierre Monnin, Mathieu d'Aquin, Davy Monticolo, Armelle Brun
In some data-sensitive fields such as education or medicine, access to public datasets is even more limited.
1 code implementation • 28 Jun 2023 • Lucas Jarnac, Miguel Couceiro, Pierre Monnin
Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop.
1 code implementation • 6 Jun 2023 • Nicolas Hubert, Heiko Paulheim, Pierre Monnin, Armelle Brun, Davy Monticolo
These models learn a vector representation of knowledge graph entities and relations, a. k. a.
2 code implementations • 1 Mar 2023 • Nicolas Hubert, Pierre Monnin, Armelle Brun, Davy Monticolo
In an extensive and controlled experimental setting, we show that the proposed loss functions systematically provide satisfying results which demonstrates both the generality and superiority of our proposed approach.
2 code implementations • 13 Jan 2023 • Nicolas Hubert, Pierre Monnin, Armelle Brun, Davy Monticolo
That is why, in this paper, we extend our previously introduced metric Sem@K that measures the capability of models to predict valid entities w. r. t.
no code implementations • Proceedings of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching co-located with the 20th International Semantic Web Conference 2021 • Viet-Phi Huynh, Jixiong Liu, Yoan Chabot, Frédéric Deuzé, Thomas Labbé, Pierre Monnin, Raphaël Troncy
In this paper, we present the latest improvements of the DAGOBAH system that performs automatic pre-processing and semantic interpretation of tables.
Ranked #1 on
Column Type Annotation
on ToughTables-WD
no code implementations • 16 Dec 2020 • Emmanuel Bresso, Pierre Monnin, Cédric Bousquet, François-Elie Calvier, Ndeye-Coumba Ndiaye, Nadine Petitpain, Malika Smaïl-Tabbone, Adrien Coulet
We propose to mine knowledge graphs for identifying biomolecular features that may enable reproducing automatically expert classifications that distinguish drug causative or not for a given type of ADR.
1 code implementation • 11 Nov 2020 • Pierre Monnin, Chedy Raïssi, Amedeo Napoli, Adrien Coulet
In this article, we propose to match nodes within a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings, in order to suggest alignment relations between nodes of a same cluster.
1 code implementation • 17 Jul 2020 • Pierre Monnin, Emmanuel Bresso, Miguel Couceiro, Malika Smaïl-Tabbone, Amedeo Napoli, Adrien Coulet
Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking.
1 code implementation • 19 Feb 2020 • Pierre Monnin, Miguel Couceiro, Amedeo Napoli, Adrien Coulet
In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar.