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.
Additional details are available on the CV.
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
Teaching Assistant
September 2024 - Present
University of Mannheim, Germany
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 2018 - December 2023
LASIGE, Faculty of Sciences, University of Lisbon, Portugal
Projects
Current Projects
CEREBRA-AI September 2026 – present
CEREBRA-AI will deliver a sovereign and trustworthy-by-design general-purpose AI platform built around four pillars: (i) neuro-symbolic reasoning core for verifiable multi-step inference; (ii) continual-learning to adopt and coordinate specialist models; (iii) embodied and relational interface through robots and digital twins; (iv) built-in trust framework compliant with AI Act.
Modelling and mapping of ontologies for cross-sectorial and public health crisis management June 2025 – present
The goal of this project is to define domain and application ontologies and link them to existing standards, to support semantic interoperability across different systems.
Past Projects
KI‑DiabetesDetektion January 2024 – July 2026
The goal of the KI-DiabetesDetektion project was to integrate indication data from various sources in a knowledge graph and apply machine learning methods to improve the early stage detection of diabetes.
Serving up‑to‑date Dynamic Knowledge Graph Embeddings January 2025 – June 2025
The goal was to develop a framework capable of periodically collecting new knowledge graph versions, computing embeddings and making them publicly available for further research in a web application.
KATY: Knowledge At the Tip of Your fingers November 2023 – December 2023
The project aimed to build a precise, personalised medicine system empowered by artificial intelligence, which predicts the response of kidney cancer to targeted therapies.
SMiLax: Semantic Mining with Linked Data November 2018 – August 2019
The goal was to improve the state of the art in semantic data mining by establishing novel methods and algorithms for the automated semantic annotation and enrichment of data, whose output will be explored by novel data mining approaches capable of capitalizing on the semantic web.
Publications and Presentations
- All
- 2026
- 2025
- 2024
- 2023
- 2022
- 2021
- 2020
- 2019
Publications:
Aligning Biomedical Texts and Knowledge Graphs: A Systematic Comparison of Lightweight Alignment Strategies
Artem Bisliouk, Elizaveta Nosova, Heiko Paulheim, Andreea Iana, and Rita T. Sousa
Accepted at KG-NeSy @ ISWC2026
2026
The role of protein embeddings for protein–protein interaction prediction with graph neural networks
Laura Balbi, Rita T. Sousa, Pedro Cotovio, Catia Pesquita
Briefings in Bioinformatics
2026
Improving Knowledge Graph Embeddings through Contrastive Learning with Negative Statements
Rita T. Sousa, Heiko Paulheim
K-CAP
2025
Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
Hamid Ahmad, Heiko Paulheim, Rita T. Sousa
ISWC 2025 Poster and Demo Track
2025
Multi-dataset and Transfer Learning Using Gene Expression Knowledge Graphs
Rita T. Sousa, Heiko Paulheim
ESWC 2025
2025
Gene Expression Knowledge Graph for Patient Representation and Diabetes Prediction
Rita T. Sousa, Heiko Paulheim
Journal of Biomedical Semantics
2025
Towards leveraging explicit negative statements in knowledge graph embeddings
Rita T. Sousa, Catia Pesquita, Heiko Paulheim
Journal of Web Semantics
2024
On the legal implications of Large Language Model answers: A prompt engineering approach and a view beyond by exploiting Knowledge Graphs
George Hannah, Rita T. Sousa, Ioannis Dasoulas, Claudia d’Amato
Journal of Web Semantics
2024
Integrating heterogeneous gene expression data through knowledge graphs for improving diabetes prediction
Rita T. Sousa, Heiko Paulheim
SeWeBMeDA 2024
2024
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:
Improving Knowledge Graph Embeddings through Contrastive Learning with Negative Statements [Slides]
Presentation @ K-CAP 2025
Bio-KGvec2go: Serving up-to-date Dynamic Biomedical KG Embeddings [Poster]
Presentation @ ISWC 2025
Multi-dataset and Transfer Learning Using Gene Expression Knowledge Graphs [Slides]
Presentation @ ESWC 2025
Serving up-to-date Dynamic Knowledge Graph Embeddings [Slides]
Presentation @ Open Science Day 2024
Integrating gene expression data through knowledge graphs for diabetes prediction [Slides]
Presentation @ SeWeBMeDA 2024
From biomedical data to knowledge [Slides]
Presentation @ MCDS Academic Speed Dating
Biomedical knowledge graph embeddings with negative statements [Slides] [Video]
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] [Video]
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
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 circumvent 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.
TrueWalks
TrueWalks learn knowledge graph embeddings, considering negative statements and their implications.
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 trains supervised ML algorithms to learn a supervised semantic similarity according to the objective similarity.
evoKGsim+
evoKGsim+ applies genetic programming over a set of semantic similarity features to obtain the best combination for a given supervised learning task.