Search Results for author: Pierre Monnin

Found 10 papers, 8 papers with code

Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning

1 code implementation28 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.

graph construction Transfer Learning

Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction

2 code implementations1 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 on three public benchmark KGs underpinned with different schemas, which demonstrates both the generality and superiority of our proposed approach.

Knowledge Graph Embedding Knowledge Graphs +1

Sem@$K$: Is my knowledge graph embedding model semantic-aware?

2 code implementations13 Jan 2023 Nicolas Hubert, Pierre Monnin, Armelle Brun, Davy Monticolo

Traditionally, the performance of KGEMs for link prediction is assessed using rank-based metrics, which evaluate their ability to give high scores to ground-truth entities.

Knowledge Graph Embedding Knowledge Graphs +1

Investigating ADR mechanisms with knowledge graph mining and explainable AI

no code implementations16 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.

Explainable Models Graph Mining +1

Discovering alignment relations with Graph Convolutional Networks: a biomedical case study

1 code implementation11 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.

Clustering Knowledge Graphs

Knowledge-Based Matching of $n$-ary Tuples

1 code implementation19 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.

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