Our world is moving into a truly digital era. The transition into digital platforms is not anymore a convenient alternative but a necessity, that affects all organizations worldwide and generates large amounts of data. The larger datasets grow the more difficult it is to understand and abstract the relevant information from them. One of the most fundamental challenges in artificial intelligence (AI) is to automatically abstract such knowledge from the data and concisely represent it, so that it can be interpreted and explained in an accurate way. Our goal is to study and develop new automated strategies for building ontologies—which are explainable knowledge representation formalisms based on logic. Ontologies are useful to express the relevant knowledge about a domain of interest. This proposal is based on learning models from computational learning theory. The project contains two methods for learning ontologies. The first employs algorithms designed for the exact learning model and the second is a differentiable way of learning ontologies in the probably approximately correct learning model.
Project leader: Ana Ozaki
Institution: Institutt for informatikk