Thesis Topics

If you want to do a bachelor or master thesis, don’t be afraid to send me an email. I have a range of topics listed below, and I can give you additional information based on your experience, skills and interests. If you have your own idea, we can discuss it to see whether it is feasible, whether I am the right supervisor, or whether there are similar directions that would be better suited. One warning: even though my expertise is in a subfield of artificial intelligence, I will not supervise theses with a focus on deep learning, large language models or image recognition. My focus is on symbolic AI, that is, knowledge representation and logic-based AI. Consequently, theses topics will have to be related to those fields.

This year, a majority of topics revolves around explanations for description logic ontologies. This is in the context of a larger project that aims at improving the experience of ontology developers through better tool supports. We have developed a range of tools for explainability already, that you can try out through the web application Evonne, as well as through the Protege plugin collection Evee. For some of the student projects, it is possible that the results can get connected to these tools. It should be noted however that the development of the user interface is not part of any project, but rather the development of better explanation methods that can then be used by these frontends.

There are also some exciting topics that are not related to explanation. Scroll down to see what is there.

Most of the project are designed in a scalable way, so that they can be adapted based on the skill sets of the student. For this reason, there are also many projects that are available to both Bachelor and Master students. This means that the project will have a different character depending on whether it is for a Bachelor or for a Master thesis. The Bachelor project would look at a simplified version of the problem, and often extend or use existing tools, while the Master thesis will be more involved, and even involve the development of new methods and prototypes.

Explanations for Ontologies

We offer a range of projects around the topic of explanations for ontologies. We focus on ontologies based on description logics or OWL. A main advantage of formulating knowledge in such a formalism is that one can use a reasoner to derive implicit information. However, not always is the result of this reasoning process easy to understand: users might wonder why something was derived (explain positive entailment), or why something was not derived (explain negative entailments). Motivated by this, different methods have been developed to provide explanations for positive and negative entailments.

Explaining Entailments using New Inference Rules

The classical way of explaining why something follows from an ontology is by providing a proof tree, that shows in small steps how the positive entailment is derived from the statements in the ontology. Such a proof is usually generated based on a set of rules. We have a tool that can process user-defined sets of inference rules to generate rules. Existing sets of rules are usually not optimized for human understanding. The aim of this project is to develop new sets of inference rules that lead to nicer proofs, and provide an evaluation and comparion with existing explanation methods based on realistic ontologies.

This project is primarily intended for Bachelor students, but if a Master student finds this topic interesting, there is also a Master project version.

Explaining Positive Entailments using Universal Models

The topic of this project is to explain queries to data that is used together with an ontology. Specifically, the user asks for instances of a concept C, and he would like to understand why individual name “a” is an answer to this query. There are ontology languages where this can be explained based on so-called universal models: a universal model is a model of the ontology that captures all entailments. The aim of this project would be to develop a method to generate explanations based on such models.

This project is suitable for Bachelor and Master students.

Explaining Missing Entailments using Counter-Examples

One way to explain a missing entailment is by providing an counter example. For example, a counterexample for the statement “every pizza is vegetarian” from an ontology about pizzas would be a pizza with a salami topping, which would be model of the concept “Pizza”, but not of “VegetarianPizza”. The topic of this project is to develop and evaluate a method for computing such counterexamples. The Master version is towards developing a new method based on existing reasoning procedures. The Bachelor version will be about improving and extending an existing method.

This project is suitable for Bachelor and Master students.

Explaining Missing Entailments using Connection-Minimal Abduction

Abduction is an approach to explain missing entailments by stating “what is missing” - namely, suggesting statements that, when added to the ontology, would make the entailment positive. There are many different conditions one can give to such an explanation to make it “useful”. This project focusses on a recently discovered criterion called “connection-minimality”. Depending on the interests, there are different directions possible. A more practically interested student would get the task of developing a new method for performing connection-minimal abduction, and compare it to the state-of-the-art. For students that are less interested in implementations, the project would be to develop a new criterion based on connection-minimality, which overcomes some of the limitations of the existing one.

This project is suitable for Bachelor and Master students.

Explaining Missing Entailments using Signature-Based Abduction

Abduction is an approach to explain missing entailments by stating “what is missing” - namely, suggesting statements that, when added to the ontology, would make the entailment positive. There is an abduction tool called LETHE-abduction that computes such explanations based on a provided signature: a vocabulary of names that are allowed to be used in the explanation. Selecting such a vocabulary is currently up to the user. The goal of this project is to investigate and compare heuristics for selecting signatures for computing a nice sequence of explanations.

This project is suitable for Bachelor and Master students.

Learning Concept Descriptions

The aim of this project is to implement and evaluate a new method for learning description logic concept descriptions from examples, based on recent advancements on non-classical reasoning. In this scenario, the ontology is used together with a set of positive and negative examples. The aim is then to generate a concept that
describes all positive, but none of the negative examples. The learning procedure will be based on logical reasoning, that is, will use logical reasoning (with the help of existing tools) to compute the concept.

There is a version of this project for both Bachelor and Master students.

Extracting Subontologies

Existing ontologies are often very large and complex, while applications are often only need a fragment of the information provided by the ontology. 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, leading to simpler ontologies, and shorter computation times.

This project is most suited for Bachelor students.

Optimizing Concept Expressions

Ontologies often contain expressions that are more complex than necessary. This is even more a problem with ontology content that is automatically generated, which appears in many applications. The aim of this project is to develop a method to optimize concept expressions by replacing them by equivalent expressions that are of minimal size. One possibility is to use ideas from concept learning.

This project is most suited for Master students.

Automated Hypothesis Generation using ABox abduction

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.

This project is intended as Master project.