Hour: 9:00 - 11:00 a.m.
Daniel Ramos da Silva, Artur Ziviani e Fabio Porto
The increasing production and availability of massive and heterogeneous data bring forward challenging opportunities. Among them, the development of computing systems capable of learning, reasoning, and inferring facts based on prior knowledge is an important task. In this scenario, knowledge bases are valuable assets for the knowledge representation and automated reasoning of diverse application domains. Especially, inference tasks on knowledge graphs (knowledge bases’ graphical representations) are increasingly important in academia and industry. In this short course, we introduce machine learning methods and techniques employed in knowledge graph inference tasks as well as discuss the technical and scientific challenges and opportunities associated with those tasks.