Thesis title: Automated Hypothesis Generation using ABox Abduction

Supervisor: Patrick Koopmann (

This project looks at the following problem: we have an ontology, as well as some data in the form of a knowledge graph of ABox. This contains our background knowledge about some domain such as medicine, or a context from robotics. We are then given a set of facts that do not follow from what we know according to our background knowledge - an observation that is somehow unexpected, for instance a description of symptoms of a patient or of an unexpected situation encountered by a robot. We then want to generate a hypothesis in the form of a set of facts that would explain the observation if added to the background knowledge. To avoid trivial answers, we assume that there is also a special vocabulary for explanations provided. This means, we want to compute a hypothesis that uses only terms from that vocabulary, but may refer also to unknown objects. This problem is called signature-based ABox abduction. The aim of this project is to develop a new method for signature-based ABox abduction based on some recent theoretical results of this problem.

The supervisor will give an introduction to the topic and the proposed idea at the beginning of the project. You can contact the supervisor if you would like to have more information on this project or would like to discuss it in more detail in person.