7 papers
A Framework for FAIR and CLEAR Ecological Data and Knowledge: Semantic Units for Synthesis and Causal Modelling
Lars Vogt, Birgitta König-Ries, Tim Alamenciak +5
Ecological research increasingly relies on integrating heterogeneous datasets and knowledge to explain and predict complex phenomena. Yet, differences in data types, terminology, 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…
GerPS-Compare: Comparing NER methods for legal norm analysis
Sarah T. Bachinger, Christoph Unger, Robin Erd +4
We apply NER to a particular sub-genre of legal texts in German: the genre of legal norms regulating administrative processes in public service administration. The analysis of such…
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…