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Tim Schopf

Faculty of Informatics
Chair of Informatics 19
Software Engineering for Business Information Systems (sebis)    

Technische Universität München 
Boltzmannstraße 3
85748 Garching bei München, Germany

Phone:  +49 89 289-17105
Fax: +49 89 289-17136


Room FMI 01.12.057 

Office hours: by appointment


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Student offers:

  • I offer motivated students the oportunity to work at the chair of Software Engineering for Business Information Systems (sebis) as student research assistant in the area of Natural Language Processing (NLP). If you are interested, please contact me with your application, including current CV and transcript of records. More information here.
  • Currently, I do not have any open thesis topics.

Curriculum Vitae

Mr. Schopf joined the chair of Software Engineering for Business Information Systems at the Technical University of Munich in September 2020. He holds a master's degree in Information Engineering and Management from Karlsruhe Institute of Technology and wrote his master's thesis at MHP - A Porsche Company about defending adversarial attacks on deep neural networks for autonomous driving by randomization and anomaly detection. He acquired his bachelor's degree in Information Systems from a joint program of the Universities of Hohenheim and Stuttgart. During his studies he also gained work experience as Data Science Intern at Daimler and as student research assistant at Karlsruhe Institute of Technology. 



As one of the organizers Tim Schopf is responsible for hackaTUM, the official annual TUM hackathon hosted by the Department of Informatics since 2016. Over the last years, it has become one of the biggest hackathons in the EU with over 1000 applications and 700 participants in the last event. 

The next edition of hackaTUM will take place in 2022!
If you are interested in sponsoring the event, contact Further information can be found at


Research Interests

  • Natural Language Processing
  • Knowledge Graphs
  • Knowledge Graph Construction 
  • Knowledge Exploration in Knowledge Graphs
  • Ontology Learning 
  • Question Answering
  • Information Extraction


Current Research Projects


Research Institution Knowledge Graph (RIKG)

Given that scientific knowledge usually is available in large quantities as unstructured texts, it is very difficult for researchers to obtain an overview of research fields or scientific domains. Similarly, it is difficult for researchers to gain insight into topics being researched at research institutions. Information about conducted research often only exists in unstructured texts on homepages or intranet pages. In addition, the websites are usually designed according to organizational structures of research institutions rather than a logical structure based on research areas. Therefore, a research area based navigation through the instutution websites is hardly possible. This makes exploration and navigation of topics being researched in an institution very difficult for external as well as for internal users. Structuring the scientific knowledge of research institutions and linking semantically related scientific domains offers researchers the potential for enhanced exploration of research areas. More here ...

Knowledge4Retail (K4R)

The sebis chair is part of the project "Knowledge4Retail", which is a winning project of the "Künstliche Intelligenz als Treiber für volkswirtschaftlich relevante Ökosysteme" programme and thus funded by the Bundesministerium für Wirtschaft und Energie (BMWi). The goal of Knowledge4Retail is to advance the digitization of retail by developing a data platform that enables the combination of online and stationary retail, serves strategic marketing and makes digital solutions available for individual customer service using a semantic digital twin. More here...



Completed Research Projects


Technology Scouting as a Service (TSaaS)

Due to the quantity and complexity of data (Big Data), the classical information gathering of engineers, for the search for technologies to solve problems in mechanical and plant engineering, no longer meets the requirements of a modern information society. This project aims to research AI-based processes that offer engineers a completely new possibility to find tailor-made solutions for their problems. For this purpose, natural language processing algorithms will be developed that semantically analyze large amounts of text, extract essential information and transfer it into an information model (knowledge graph) in order to capture and link problems and technologies. By means of matching, solutions for a wide range of problems can then be generated by engineers at the push of a button. More here...



Teaching (in reverse chronological order)

Term Level Title Type Role
SS 22 Master IN2107, IN4816: Natural Language Processing - Methods and Applications Seminar Advisor
SS 22 Master

IN2087Software Engineering for Business Applications - Web Applications Engineering

Lab Course Advisor
WS 21/22  Master

IN2106, IN7106SEBA Lab Course

Lab Course Advisor 
WS 21/22 Bachelor & Master

IN2235Software Engineering in der industriellen Praxis

Lecture Organizer
SS 21 Master

IN2087Software Engineering for Business Applications - Web Applications Engineering

Lab Course Advisor
WS 20/21 Bachelor & Master IN2235Software Engineering in der industriellen Praxis Lecture Advisor
WS 20/21 Master IN2106, IN7106SEBA Lab Course Lab Course Advisor

Publications (in reverse chronological order)


Schopf, Tim; Weinberger, Peter; Kinkeldei, Thomas; Matthes, Florian

Towards Bilingual Word Embedding Models for Engineering, MSIE 2022, 4th International Conference on Management Science and Industrial Engineering, Chiang Mai, Thailand, 2022

DOI: 10.1145/3535782.3535835



Schopf, Tim; Braun, Daniel; Matthes, Florian

Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics, In Proceedings of the 17th International Conference on Web Information Systems and Technologies, Online, 2021

DOI: 10.5220/0010710300003058


Braun, Daniel; Klymenko, Oleksandra; Schopf, Tim; Akan, Kaan; Matthes, Florian

The Language of Engineering: Training a Domain-Specific Word Embedding Model for Engineering, MSIE 2021: 3rd International Conference on Management Science and Industrial Engineering, Osaka, Japan, 2021

DOI: 10.1145/3460824.3460826