1. List the most common similarity algorithms, and if possible, some differences among them; 2. List some techniques for cluster detection; 3. Explain in a few sentences what is reasoning and what is its purpose in the graph context. 4. How are biomedical ontologies or controlled vocabularies utilised to represent entities and relationships? 5. What are the challenges in generating high-quality knowledge graphs from unstructured biomedical text (Give some examples of NLP usage in this task)? 6. How can ontology linking help address issues like entity disambiguation? 7. How LLMs could be integrated to optimise biological knowledge graphs? 8. Define Relationship Extraction (RE) 9. Define Entity Resolution (ER) 10. Define NER (Named Entity Recognition)
