Workshop Schedule

09:00 - 9:25 Opening

09:25 - 10:25 Keynote: Dr Catia Pesquita

Short Bio

Catia Pesquita is a Professor of Computer Science at the Faculty of Sciences of the University of Lisbon and a Senior Researcher at LASIGE where she leads the Research Line of Excellence in Biomedical and Health Informatics. She has a multidisciplinary background in Biology and Computer Science, and develops her research at the intersection between Semantic Web and Data Mining, focusing on applications in the life and health sciences. She is also vice-president of the Biodata.pt association for the valorization of the biological data generated by Portuguese Science, and is involved in activities to promote computer science career paths to young women.

Title: Powering Biomedical Artificial Intelligence with a Holistic Knowledge Graph

Biomedical AI applications increasingly rely on multi-domain and heterogeneous data, especially in areas such as personalised medicine and systems biology. Biomedical Ontologies are a golden opportunity in this area because they add meaning to the underlying data which can be used to support heterogeneous data integration, provide scientific context to the data augmenting AI performance, and afford explanatory mechanisms allowing the contextualization of AI predictions. In particular, ontologies and knowledge graphs support the computation of semantic similarity between objects, providing an understanding of why certain objects are considered similar or different. This is a basic aspect of explainability and is at the core of many machine learning applications. However, when data covers multiple domains, it may be necessary to integrate different ontologies to cover the full semantic landscape of the underlying data.

In this talk I will present our recent work on building an integrated knowledge graph that is based on the semantic annotation and interlinking of heterogeneous data into a holistic semantic landscape that supports semantic similarity assessments. In this talk I will discuss the challenges in building the knowledge graph from public resources, the methodology we are using and the road-ahead in biomedical ontology and knowledge graph alignment as AI becomes an integral part of biomedical research.

10:30 – 11:00 Coffee Break

11:00 - 12:00 Session 1: Healthcare and Life Sciences Knowledge Graphs

• Niclas Heilig, Jan Kirchhoff, Florian Stumpe, Joan Plepi, Lucie Flek and Heiko Paulheim. Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

• Jeffrey Sardina and Declan O'Sullivan. Structural Characteristics of Knowledge Graphs Determine the Quality of Knowledge Graph Embeddings Across Model and Hyperparameter Choices

12:00 - 13:00 Session 2: Intelligent Systems and Application in Healthcare

• Elaine Taylor Whilde, Janine Lane, Chris Dickson, Syeda Mah E Fatima, Qurratal Ain Fatimah and Ali Hasnain. Woubot: A personalised predictive AI system for hard to heal wounds

• Alba Catalina Morales Tirado, Enrico Daga and Enrico Motta. CONRAD - Health Condition Radar an Intelligent System for Emergency Support

13:00 – 14:00 Lunch Break

14:00 - 15:30 Session 3: Ontologies in Healthcare and Life Sciences

• Alba Catalina Morales Tirado, Enrico Daga and Enrico Motta. HECON: Health Condition Evolution Ontology

• Sara Diaz, Silvio Cardoso, Marcos Da Silveira and Cédric Pruski. DynDiff: A Tool for Comparing Versions of Large Ontologies

15:30 – 16:00 Coffee Break

16:00 - 17:20 Session 4: Healthcare and Life Sciences Data Interoperability

• Rita T. Sousa, Sara Silva and Catia Pesquita. Towards Supervised Biomedical Semantic Similarity

• Derek Corrigan, Qurratal Ain Fatimah, Syeda Mah E Fatima and Ali Hasnain. Let’s FAIRify Electronic Health Records (EHRs)

• Beyza Yaman, Kris McGlinn, Lucy Hederman, Declan O'Sullivan and Mark A. Little. Towards a rare disease registry standard: semantic mapping of common data elements between FAIRVASC and the European Joint Programme for Rare Disease

17:20- 17:30 Closing