Por Cátia Pesquita (LASIGE).
Most of our data is about positive facts: a patient has hypertension, the BRCA2 gene is related to breast cancer, Lisbon is the capital of Portugal. In many applications, the assumption is made that everything that is not stated is false (the closed-world assumption), but for real-world and critical domains, such as those in biomedical research and healthcare, conflating what we don’t know with what is false carries a high risk: patients with unreported symptoms can be given the wrong diagnosis, drugs with unknown interactions can be prescribed in tandem. Knowledge graph-based machine learning applications are a prime example of this mismatch between algorithms that operate under the closed-world assumption and real datasets that are open-world. In this talk, I will discuss the challenges faced by machine learning and artificial intelligence applications when the difference between a negative fact and an unknown fact is crucial. The discussion will be supported by real use cases in biomedical research.
