Prof. Dr. Stefan Decker - Theses
Bachelor
Developing a Data Annotation Tool for Scientific Data Management - Open
Supervised by Prof. Dr. Stefan Decker; Advisor(s):
Dr. Oya Deniz Beyan
Semantic technologies and RDF data representation can improve the reusability of scientific data and enable scientist to reproduce the experiments. However there is no tool to support researcher for making their data semantically interpretable by computers. Aim of the thesis is to understand the benefits of semantic web technologies for reproducible research and develop a tool which can convert experimental data to RDF by annotating with selected data models.
Bachelor, Master
Entwicklung einer Co-Simulationsumgebung zur Abbildung des Prozessdatenverkehrs von intelligenten Energienetzen - Open
Supervised by Prof. Dr. Stefan Decker, Dr-Ing. Michael Andres
Bachelor, Master
Entwicklung eines Systems zur Angriffserkennung in intelligenten Energienetzen - Open
Supervised by Prof. Dr. Stefan Decker, Dr-Ing. Michael Andres
Master
Exploring Unknown Environments - Finding Pollution in Underground Pipes - Open
Supervised by Prof. Dr.-Ing. Gerd Ascheid, Prof. Dr. Stefan Decker; Advisor(s):
Dr. Michael Cochez,
Ahmed Hallawa
Bachelor, Master
Modellierung und Analyse der Anwendbarkeit von Blockchain Technologien in der Energieversorgung - Open
Supervised by Prof. Dr. Stefan Decker, Dr-Ing. Michael Andres
Bachelor
Arden Syntax in Use of Data Analysis Pipelines - Development of a DSL Based on the Arden Syntax, supporting recruitment for clinical trails - Running
Supervised by Prof. Dr. Stefan Decker, Dr. rer. nat. Cord Spreckelsen; Advisor(s):
Dr. Oya Deniz Beyan
Master
Deep Learning-based Knee Osteoarthritis Diagnosis from Radiographs and Magnetic Resonance Images - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s):
Dr. Oya Deniz Beyan,
Dr. Michael Cochez,
Md. Rezaul Karim, M.Sc.
In this thesis, the student investigates the use of deep learning techniques (especially computer vision) to perform diagnosis of osteoarthritis. The input to the system are both radiographs (X-RAY) and magnetic resonance images (MRI).
Master
Design and Implementation of an adaptable visual query tool for graph based health data - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s):
692050c6199c8bbfb9be2189e82ff904
Master
Developing a machine-actionable process model for ethical approval workflows - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s):
Dr. Oya Deniz Beyan
Bachelor
Dynamic Embeddings of Evolving Knowledge Graphs - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s):
Dr. Michael Cochez,
Dr. Florian Lemmerich
The goal of this Bachelor thesis is the research of updating KG embeddings with new information in order to obtain a dynamic and stable embedding of the fast-evolving KG while reducing the computational effort.
Master
Graph-Structured Query Construction for Natural Language Questions - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s):
Dr. Michael Cochez
Graph-structured queries provide an efficient means to retrieve desired data
from large-scale knowledge graphs. However, it is difficult for non-expert users to
write such queries, and users prefer expressing their query intention through
natural language questions. Recently, an increasing effort is being exerted to
construct graph-structured queries for given natural language questions. At the
core of the construction is to deduce the structure of the target query and retrieve
vertices/edges of the underlying knowledge graph which constitute the query.
Existing query construction methods rely on conventional graph-based algorithms
and question understanding techniques, which lead to inefficient and degraded
performances facing complicated natural language questions over knowledge
graphs with large scales. In this thesis, we focus on this problem and propose
novel construction models standing on recent knowledge graph embedding
techniques. Extensive experiments were conducted on question answering
benchmark datasets, and the results demonstrate that our models outperform
baselines in terms of effectiveness and efficiency.
Bachelor
Machine Learning for Anonymization of Unstructured Text - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s):
Dr. Michael Cochez
This thesis addresses the problem of identifying personal information in unstructured text using supervised Machine Learning (ML). The final application should be able to recognize and annotate the tokens that make up personal data in an English input text as accurately as possible. First, supervised learning methods, suitable for the task, will be identified. Then, models based on the most promising approaches will be designed and implemented. For comparison, suitable evaluation metrics have to be determined. Finally, the approaches are compared and evaluated against a baseline and each other.
Master
Modeling for Street Level Crime Prediction - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s):
Dr. Michael Cochez,
Cristina Kadar,
Raquel Rosés Brüngger
The aim of this master thesis is to build a predictive model of crime at street level for a Swiss city, including a tool implementation for visualizing the data and results.
Master
Patterns for Integrating Rule Based and Process Based Model Components of Computerized Clinical Guidelines - Running
Supervised by Prof. Dr. Stefan Decker, Dr. rer. nat. Cord Spreckelsen; Advisor(s):
692050c6199c8bbfb9be2189e82ff904
Master
Privacy Attack on Social Networks Using Network Embeddings - Running
Supervised by Prof. Dr. Markus Strohmaier, Prof. Dr. Stefan Decker; Advisor(s):
Dr. Florian Lemmerich,
Dr. Michael Cochez
Abstract. A company that runs a social network trains a node embedding on the network where each account is represented by one node. One user deletes his account. Thus, the company is legally required to remove all private information of that user. This includes the node associated with the user’s account and the vector representation of that node that is generated by the embedding. The company, however, does likely not delete the vector representations of the other nodes even though the removed node was used during training of these. Is it possible to identify the neighbors of the removed node? Which kinds of neighbors can be identified best, which cannot be identified? First results suggest that the identification of neighbors works well for some kind of nodes and is more difficult for others.
Professorship for Christoph Quix 