46 citations · 70 across the 13 of their papers we have counts for
14 papers
LLM-Assisted Discovery of Typed Semantic Links for Ontology Network Construction
Nouha Hayouni, Sheeba Samuel, Alsayed Algergawy
Constructing typed, justified semantic links between ontologies is essential for enabling interoperability across heterogeneous and interdisciplinary knowledge domains. However, ma…
Learning to be Reproducible: Custom Loss Design for Robust Neural Networks
Waqas Ahmed, Sheeba Samuel, Kevin Coakley +2
To enhance the reproducibility and reliability of deep learning models, we address a critical gap in current training methodologies: the lack of mechanisms that ensure consistent a…
Explainability of Deep Learning-Based Plant Disease Classifiers Through Automated Concept Identification
Jihen Amara, Birgitta König-Ries, Sheeba Samuel
While deep learning has significantly advanced automatic plant disease detection through image-based classification, improving model explainability remains crucial for reliable dis…
Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publications
Vamsi Krishna Kommineni, Birgitta König-Ries, Sheeba Samuel
Deep Learning (DL) techniques are increasingly applied in scientific studies across various domains to address complex research questions. However, the methodological details of th…
Enhancing Explainability in Multimodal Large Language Models Using Ontological Context
Jihen Amara, Birgitta König-Ries, Sheeba Samuel
Recently, there has been a growing interest in Multimodal Large Language Models (MLLMs) due to their remarkable potential in various tasks integrating different modalities, such as…
Evaluating the method reproducibility of deep learning models in the biodiversity domain
Waqas Ahmed, Vamsi Krishna Kommineni, Birgitta König-Ries +3
Artificial Intelligence (AI) is revolutionizing biodiversity research by enabling advanced data analysis, species identification, and habitats monitoring, thereby enhancing conserv…
From human experts to machines: An LLM supported approach to ontology and knowledge graph construction
Vamsi Krishna Kommineni, Birgitta König-Ries, Sheeba Samuel
The conventional process of building Ontologies and Knowledge Graphs (KGs) heavily relies on human domain experts to define entities and relationship types, establish hierarchies,…
Reproducible Domain-Specific Knowledge Graphs in the Life Sciences: a Systematic Literature Review
Samira Babalou, Sheeba Samuel, Birgitta König-Ries
Knowledge graphs (KGs) are widely used for representing and organizing structured knowledge in diverse domains. However, the creation and upkeep of KGs pose substantial challenges.…
Concept explainability for plant diseases classification
Jihen Amara, Birgitta König-Ries, Sheeba Samuel
Plant diseases remain a considerable threat to food security and agricultural sustainability. Rapid and early identification of these diseases has become a significant concern moti…
Computational reproducibility of Jupyter notebooks from biomedical publications
Sheeba Samuel, Daniel Mietchen
Jupyter notebooks facilitate the bundling of executable code with its documentation and output in one interactive environment, and they represent a popular mechanism to document an…
Computational reproducibility of Jupyter notebooks from biomedical publications
Sheeba Samuel, Daniel Mietchen
Jupyter notebooks allow to bundle executable code with its documentation and output in one interactive environment, and they represent a popular mechanism to document and share com…
Toward a Framework for Integrative, FAIR, and Reproducible Management of Data on the Dynamic Balance of Microbial Communities
Luiz Gadelha, Martin Hohmuth, Mahnoor Zulfiqar +5
The increasing volumes of data produced by high-throughput instruments coupled with advanced computational infrastructures for scientific computing have enabled what is often calle…
Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles
Sheeba Samuel, Frank Löffler, Birgitta König-Ries
Machine learning (ML) is an increasingly important scientific tool supporting decision making and knowledge generation in numerous fields. With this, it also becomes more and more…
ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks
Sheeba Samuel, Birgitta König-Ries
Computational notebooks have gained widespread adoption among researchers from academia and industry as they support reproducible science. These notebooks allow users to combine co…