7 citations · 11 across the 8 of their papers we have counts for
6 papers · 1 filter
Improving Knowledge Graph Embeddings through Contrastive Learning with Negative Statements
Rita T. Sousa, Heiko Paulheim
Knowledge graphs represent information as structured triples and serve as the backbone for a wide range of applications, including question answering, link prediction, and recommen…
Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
Hamid Ahmad, Heiko Paulheim, Rita T. Sousa
Knowledge graphs and ontologies represent entities and their relationships in a structured way, having gained significance in the development of modern AI applications. Integrating…
Multi-dataset and Transfer Learning Using Gene Expression Knowledge Graphs
Rita T. Sousa, Heiko Paulheim
Gene expression datasets offer insights into gene regulation mechanisms, biochemical pathways, and cellular functions. Additionally, comparing gene expression profiles between dise…
Integrating Heterogeneous Gene Expression Data through Knowledge Graphs for Improving Diabetes Prediction
Rita T. Sousa, Heiko Paulheim
Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the…
Explainable Representations for Relation Prediction in Knowledge Graphs
Rita T. Sousa, Sara Silva, Catia Pesquita
Knowledge graphs represent real-world entities and their relations in a semantically-rich structure supported by ontologies. Exploring this data with machine learning methods often…
Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies
Susana Nunes, Rita T. Sousa, Catia Pesquita
Ontology-based approaches for predicting gene-disease associations include the more classical semantic similarity methods and more recently knowledge graph embeddings. While semant…