Resumé
Rita T. Sousa is currently working as a researcher on the KI-Diabetes Detection Project at the Data and Web Science Group, University of Mannheim, Germany. Her research interests include knowledge graphs, machine learning and biomedical applications.
Education
PhD in Informatics
2019 - 2024
Faculty of Sciences of the University of Lisbon, Portugal
International Semantic Web Research Summer School
July 2023
Bertinoro, Italy
Master's Degree in Bioinformatics and Computational Biology
2017 - 2019
Faculty of Sciences of the University of Lisbon, Portugal
Bachelor's Degree in Health Sciences
2014 - 2017
University of Lisbon, Portugal
Professional Experience
Researcher
January 2024 - Present
Data and Web Science Group, University of Mannheim, Germany
Visiting researcher
September 2022 - November 2022
Data and Web Science Group, University of Mannheim, Germany
Teaching Assistant of Database Management
February 2022 - July 2023
Católica Lisbon School of Business and Economics, Portugal
Researcher
September 2019 - 2023
LASIGE, Faculty of Sciences, University of Lisbon, Portugal
Publications
- All
- 2024
- 2023
- 2022
- 2021
- 2020
- 2019
Explaining protein–protein interactions with knowledge graph-based semantic similarity
Rita T. Sousa, Sara Silva, Catia Pesquita
Computers in Biology and Medicine
2024
Multi-domain Knowledge Graph Embeddings for Gene-Disease Association Prediction
Susana Nunes, Rita T. Sousa, Catia Pesquita
Journal of Biomedical Semantics
2023
Biomedical Knowledge Graph Embeddings with Negative Statements
Rita T. Sousa, Sara Silva, Heiko Paulheim, Catia Pesquita
ISWC 2023
2023
Explainable representations for relation prediction in knowledge graphs
Rita T. Sousa, Sara Silva, Catia Pesquita
KR 2023
2023
Supervised Biomedical Semantic Similarity
Rita T. Sousa, Sara Silva, Catia Pesquita
IEEE Access
2023
Benchmark datasets for biomedical knowledge graphs with negative statements
Rita T. Sousa, Sara Silva, Catia Pesquita
SeWeBMeDA 2023
2023
Explaining artificial intelligence predictions of disease progression with semantic similarity
Susana Nunes, Rita T. Sousa, Filipa Serrano, Ruben Branco, Diogo F. Soares, Andreia S. Martins, Eleonora Auletta, Eduardo N. Castanho, Sara C. Madeira, Helena Aidos, Catia Pesquita
CLEF
2022
Towards Supervised Biomedical Semantic Similarity
Rita T. Sousa, Sara Silva, Catia Pesquita
SeWeBMeDA 2022
2022
The Supervised Semantic Similarity Toolkit
Rita T. Sousa, Sara Silva, Catia Pesquita
ESWC 2022 Poster and Demo Track
2022
Explaining Protein-Protein Interaction Predictions with Genetic Programming
Rita T. Sousa, Sara Silva, Catia Pesquita
Evo* 2022 Late-Breaking Abstracts
2022
Is there data leakage in protein-protein interaction prediction using knowledge graphs?
Rita T. Sousa, Sara Silva, Catia Pesquita
ISWC Poster and Demo Track
2021
Predicting gene-disease associations with knowledge graph embeddings over multiple ontologies
Susana Nunes, Rita T. Sousa, Catia Pesquita
Bio-Ontologies ISMB
2021
A collection of benchmark data sets for knowledge graph-based similarity in the biomedical domain
Carlota Cardoso,Rita T. Sousa, Sebastian Köhler, Catia Pesquita
Database
2020
evoKGsim+: a framework for tailoring Knowledge Graph-based similarity for supervised learning
Rita T. Sousa, Sara Silva, Catia Pesquita
ESWC Poster and Demo Track
2020
A collection of benchmark data sets for knowledge graph-based similarity in the biomedical domain
Carlota Cardoso, Rita T. Sousa, Sebastian Köhler, Catia Pesquita
ESWC Poster and Demo Track
2020
Evolving knowledge graph similarity for supervised learning in complex biomedical domains
Rita T. Sousa, Sara Silva, Catia Pesquita
BMC Bioinformatics
2019
Presentations
From Biomedical Data to Knowledge [Slides]
Presentation @ MCDS Academic Speed Dating
Biomedical Knowledge Graph Embeddings with Negative Statements [Slides]
Presentation @ ISWC 2023
Explainable representations for relation prediction in knowledge graphs [Slides]
Presentation @ KR 2023
Benchmark datasets for biomedical knowledge graphs with negative statements [Slides]
Presentation @ SeWeBMeDA 2023
Can ChatGP explain protein interactions better than Knowledge Graphs? [Poster]
Presentation @ Workshop LASIGE 2023
Towards Supervised Biomedical Semantic Similarity [Slides]
Presentation @ SeWeBMeDA 2022
The Supervised Semantic Similarity Toolkit [Poster]
Presentation @ ESWC 2022
Explaining Protein-Protein Interaction Predictions with Genetic Programming [Slides] [Poster]
Presentation @ Evo* 2022 Late-Breaking Abstracts
Is there data leakage in protein-protein interaction prediction using knowledge graphs? [Poster]
Presentation @ ISWC 2021
evoKGsim+: a framework for tailoring Knowledge Graph-based similarity for supervised learning [Poster]
Presentation @ ESWC 2020
Tools and Datasets
Benchmark datasets for KG-based similarity
Collection of 21 benchmark data sets for evaluating semantic similarity measures for large biomedical knowledge graphs and ontologies.
Benchmark datasets with negative statements
Collection of datasets for relation prediction tasks that aim at circumventing the difficulties in building benchmarks for knowledge graphs with negative statements.
SEEK
SEEK generates explainable embedding-based representations to support relation prediction in knowledge graphs.
evoKGsim+
evoKGsim applies genetic programming over a set of semantic similarity features to obtain the best combination for a given supervised learning task.
KGsim2vec
KGsim2vec uses aspect-oriented semantic similarity features to obtain explainable representations of pairs of entities in a knowledge graph for relation prediction.
The Supervised Semantic Similarity Toolkit
The toolkit train supervised ML algorithms to learn a supervised semantic similarity according to the objective similarity.