Thesis title: Extracting Sub-Ontologies

Supervisor: Patrick Koopmann (

Ontologies are an important representation formalism for symbolic AI systems. Ontologies that are formulated in OWL allow to usage of reasoning systems such as HermiT or ELK to infer implicit information from an ontology, or from a dataset that is combined with the ontology. Modern ontologies are often very large, and can contain 10,000s and even 100,000s of statements, which makes it hard to handle them. In many applications, not all of the content of an ontology is relevant, so that it would make sense to extract a smaller ontology to use instead of the original, large ontology. A subontology is a smaller and ideally simpler ontology that covers all relevant information from the ontology for a user-provided set of terms of interest. There are different techniques (module extraction, uniform interpolation) that can be used to extract subontologies. The aim of this project is to investigate and evaluate heuristics to improve the performance of these methods, or develop a new approach that works by combining them, with the aim of obtaining simpler ontologies and/or shorter computation times.

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.