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.

Table of contents

KAI Theses

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.

Information Extraction

Supervisor: Benno Kruit (b.b.kruit@vu.nl), Ilaria Tiddi (i.tiddi@vu.nl), Lise Stork (l.stork@vu.nl)

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.

Ontologies, Knowledge Engineering

Supervisor: Romana Pernisch (r.pernisch@vu.nl), Ilaria Tiddi (i.tiddi@vu.nl)

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.

Argument and Rule Mining

Supervisor: Loan Ho (t.t.l.ho@vu.nl), Lise Stork (l.stork@vu.nl)

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:

Robotics and Knowledge Representation

Supervisor: Mark Adamik (m.adamik@vu.nl), Ilaria Tiddi (i.tiddi@vu.nl)

These projects look at the intersection of robotics and knowledge graphs. Knowledge of Robotic Operating System (ROS) is a plus:

Explanations and Narratives

Supervisors: Lise Stork (l.stork@vu.nl), Ilaria Tiddi (i.tiddi@vu.nl)

The following topics are aimed at providing a more human-like AI, by creating explanations or creating narratives.

Question Answering

Supervisors: Benno Kruit (b.b.kruit@vu.nl), Stefan Schlobach (k.s.schlobach@vu.nl)

QA is a very broad topic. We, however, focus on QA over structured data in various forms:

Multi-lingual problems

Supervisors: Benno Kruit (b.b.kruit@vu.nl)

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:

Semantics of Deep Learning Methods

Supervisor: Ilaria Tiddi (i.tiddi@vu.nl)



Supervisor: Romana Pernisch (r.pernisch@vu.nl)

Elsevier is offering many theses, which were presented at the VU Theses Fair on the 11th November. The list can be found here. Following theses from the list would potentially supervised by Romana:

CFLW Cyber Strategies

Supervisors: Eljo Haspels (eljo.haspels@cflw.com), Romana Pernisch (r.pernisch@vu.nl)

CFLW is a tech startup from the Netherlands, founded at the end of 2019, based in The Hague. They develop intelligence services for law enforcement agencies, cybersecurity agencies and financial/fintech organizations. Their core product is Dark Web Monitor, which is used by various agencies around the world. For more details on CFLW see their website

Internship details



CFWL is offering two theses, follow link for more details on the projects:


Supervisors: Romana Pernisch (r.pernisch@vu.nl), Ilaria Tiddi (i.tiddi@vu.nl)

Lareb is a Pharmacovigilance Research Lab studying the effects of drugs over human bodies. The goal of this thesis is to extract and model some domain data on drug reactions so that link prediction approaches can be deployed over this data. Additionally, there is also interest in aligning the extracted model with existing Knowledge Graphs on drugs. BSc or MSc.


Supervisor: Ilaria Tiddi

We will be offering projects around KGs, ML and Hybrid Intelligence in collaboration with Accenture.

Triply DB

Supervisors: Kathrin Dentler (kathrin.dentler@triply.cc), Ilaria Tiddi (i.tiddi@vu.nl)

Triply is a company offering infrastructure solutions for knowledge graph-based data. There are several project available in collaboration with Triply DB on using Machine Learning and NLP over large scale KGs. Group work is possible and projects can be either BSc or MSc.