At KAI, we set clear expectations for students. We want to make sure students know what to expect from us as their supervisors. We have prepared a short document which touches upon some important points like meetings, planning and writing of your thesis.
You can find the slides presented at the MSc AI Thesis event here. Most of the topics below can be investigated by either BSc or MSc AI students. We also welcome groups of students working on the same or similar topic.
If you are interested in one of the projects below, please contact the supervisor(s) listed to receive more information about the topics. Where available, have a look at the detailed description first. Also, keep in mind that all theses can be shaped to accomodate your interests.
Supervisor: Benno Kruit (email@example.com), Ilaria Tiddi (firstname.lastname@example.org), Lise Stork (email@example.com), Ritten Roothaert (firstname.lastname@example.org)
We offer multiple projects under the umbrella of information extraction with varying foci. Information extraction focuses on generating structured data from unstructured inputs in an automated manner. The input as well as the output can vary based on the application or end usage of the extracted data.
Supervisor: Romana Pernisch (email@example.com)
Ontologies model specific domains. As domains evolve over time, ontologies have to be changed as well. Not only are the ontologies themsevels affected but also applications using those ontologies for various purposes. We have multiple theses in this domain.
Supervisor: Ilaria Tiddi (firstname.lastname@example.org), Lise Stork (email@example.com)
Supervisor: Loan Ho (firstname.lastname@example.org), Lise Stork (email@example.com), Atefeh Keshavarzi(firstname.lastname@example.org)
There are multiple projects in the domain of argument mining with different objectives:
Rule mining, similarly to information extraction, aims at finding structures. In this case, we want to learn rules that describe the data best to help us understand it better. There are multiple projects that involve rule mining:
Supervisor: Ilaria Tiddi (email@example.com), Mark Adamik (firstname.lastname@example.org)
These projects look at the intersection of robotics and knowledge graphs. Knowledge of Robotic Operating System (ROS) is a plus:
Supervisors: Lise Stork (email@example.com), Ilaria Tiddi (firstname.lastname@example.org)
The following topics are aimed at providing a more human-like AI, by creating explanations or creating narratives.
Semantic robustness of Language Models with causal inference. See below.
Supervisors: Benno Kruit (email@example.com), Stefan Schlobach (firstname.lastname@example.org), Lise Stork (email@example.com)
QA is a very broad topic. We, however, focus on QA over structured data in various forms:
Supervisors: Benno Kruit (firstname.lastname@example.org)
Even though these topics would also fit under different topics already discribed above, we wanted to highlight them as they are both addressing the problem of multiple languages in different tasks:
Supervisor: Ilaria Tiddi (email@example.com)
Supervisor: Patrick Koopmann (firstname.lastname@example.org)
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 (Bsc): One 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.
Explaining Positive Entailments using Universal Models (Bsc,Msc): 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 using a special type of models called universal models. The aim of this project is to develop a method to generate explanations based on such models.
Explaining Missing Entailments using Counter-Examples (Bsc,Msc): 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.
Explaining Missing Entailments using Connection-Minimal Abduction (Bsc,Msc): 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.
Explaining Missing Entailments using Signature-Based Abduction (Bsc): 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.
We offer a range of further theses topics from the areas of ontologies and ontology languages. These topics are particularly interesting for students who like ontologies and/or logics.
Learning Concept Descriptions From Examples (Bsc,Msc):
The aim of this project is to implement and evaluate a new method for
learning 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.
Extracting Subontologies (Bsc) Existing ontologies are often very large and complex, while applications are often only need a fragment of the information provided by the 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.
Optimizing Concept Expressions (Bsc,Msc): 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. We recently developed a method that can do this for a language of rather limited expressivity. The student will extend this framework, possibly using techniques from concept learning.
Automated Hypothesis Generation using ABox abduction (Msc): 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.
Make sure to first check out the official internship process!
We suggest you to pick a topic from the ones above, and adapt it to the company needs in agreement with your supervisors (both from the company and from the group). We do have contacts in several companies and research centres to do experiences abroad. For more information, contact Ilaria Tiddi (email@example.com)*