10 citations · 11 across the 7 of their papers we have counts for
7 papers
Hallucination or Creativity: How to Evaluate AI-Generated Scientific Stories?
Alex Argese, Pasquale Lisena, Raphaël Troncy
Generative AI can turn scientific articles into narratives for diverse audiences, but evaluating these stories remains challenging. Storytelling demands abstraction, simplification…
Integrating Causal Reasoning into Automated Fact-Checking
Youssra Rebboud, Pasquale Lisena, Raphael Troncy
In fact-checking applications, a common reason to reject a claim is to detect the presence of erroneous cause-effect relationships between the events at play. However, current auto…
Exploring Molecular Odor Taxonomies for Structure-based Odor Predictions using Machine Learning
Akshay Sajan, Stijn Sluis, Reza Haydarlou +6
One of the key challenges to predict odor from molecular structure is unarguably our limited understanding of the odor space and the complexity of the underlying structure-odor rel…
Improving (Re-)Usability of Musical Datasets: An Overview of the DOREMUS Project
Pasquale Lisena, Manel Achichi, Pierre Choffé +4
DOREMUS works on a better description of music by building new tools to link and explore the data of three French institutions. This paper gives an overview of the data model based…
One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization
Silvia Terragni, Ismail Harrando, Pasquale Lisena +2
Topic models are statistical methods that extract underlying topics from document collections. When performing topic modeling, a user usually desires topics that are coherent, dive…
Two Stages Approach for Tweet Engagement Prediction
Amine Dadoun, Ismail Harrando, Pasquale Lisena +2
This paper describes the approach proposed by the D2KLab team for the 2020 RecSys Challenge on the task of predicting user engagement facing tweets. This approach relies on two dis…
Linked Open Data Validity -- A Technical Report from ISWS 2018
Tayeb Abderrahmani Ghor, Esha Agrawal, Mehwish Alam +68
Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially a…