Ndeye-Emilie Mbengue

Subject Title: Abstraction and analogical reasoning to improve knowledge representation in low-resource languages

Supervisers:Fabien Gandon, Pierre Monnin and Miguel Couceiro

Specialization in Artificial Intelligence and Big Data. Degree in Computer Science and Digital Sciences obtained Sept 2024.

International exchange program: Advanced courses in Optimization, Time Series, Deep Learning, and Advanced Statistics.

0009-0002-1289-6417

"In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs"

ESWC 2026 – Doctoral Consortium.

"Which Are the Low-Resource Languages of the Semantic Web?"

ESWC 2026 – Poster and Demo Session.

"Which Pairs to Choose? Exploring Analogical Competency for Knowledge Graph Pruning"

Workshop on Analogical Reasoning for Machine Learning (IARML@IJCAI).

• Software platform maintenance for Wikidata knowledge graph pruning using analogical classification models.
• Training and evaluation of Deep Learning models (LSTM, CNN, BERT) for analogy detection and Target Sense Verification.

• Analysis of the environmental impact of digital services (LCA, ISO standards).