Tobias Koopmann, M.Sc.

Chair of Data Science (Informatik X)
University of Würzburg
Am Hubland
97074 Würzburg
Germany

Email: koopmann@informatik.uni-wuerzburg.de

Phone: (+49 931) 31 - 89363

Office: Room B100 (Computer Science Building M2)

Fingerprint: CBC7 EACE BBF3 8CF8 EB3F 3478 A5FB FAA7 8F34 AD4E

Projects and Research Interests

I joined the DMIR group for my PhD studies after receiving my masters degree in Computer Science at the university of Würzburg in early 2019. At first, i was working on click trail analysis and human behavior prediction in the web based on Wikipedia click trails.

I started to work in the REGIO project. My contribution in the project is to analyse locally successful research cooperations based on different properties of the co-author network. For this we leverage the Bayesian approach HypTrails and apply it on graph- structured data. End 2021 after the project has been successfully finalized, I started working on the HydrAS project, which is a natural succession for my research topic.

Current Thesis/Practica Topics

Linking Up: Recommendation of Co-Authorship using Hypergraph Networks

Are you a budding scientist searching for a topic to delve into? Do you possess a sharp mind and a hunger for knowledge? Then, look no further! Our esteemed institution is searching for candidates to undertake a thesis that utilizes hypergraph neural networks to prognosticate co-authorships between scientific authors. Your work may revolutionize scientific collaboration as we know it! Apply now and join the ranks of the elite few who can claim to have contributed to the progress of science! And don't forget to thank me in your thesis acknowledgement section.

Feng, Y., You, H., Zhang, Z., Ji, R., & Gao, Y. (2019, July). Hypergraph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 3558-3565).

Options: Ma Praktika, Ma Thesis

Weights Watchers: Using Deep Learning to Fine-tune HypTrails Hypothesis Mixtures

We require bright, ambitious students who are willing to undertake the task of developing a revolutionary method for optimizing the mixtures of hypotheses explaining human navigation and behavior. And, no slacking off. We expect you to use the latest and greatest techniques in deep learning to accomplish this task.

Your goal is to tackle the challenge of tuning the weighted combination of hypotheses, which are used as a prior to a Bayesian framework called HypTrails. If successful, you'll contribute to an exciting new era of human behavior research.

But, let's not forget the most important thing - you must be willing to face the challenges head-on and never give up. Remember, failure is not an option, and I won't tolerate any half-hearted efforts.

Options: Bachelor Thesis, Master Praktika

From Ancient to modern: Tackling the big scary world of database migration

Greetings prospective students, do you have a passion for database updates? Well then, have I got a project for you! We need to modernize our ancient MySQL database powering the Website Bibsonomy, which contains over 200 GB of data, by updating it to something not that ancient, like MariaDB or Postgres DB. Your task will be to change the queries from the current backend to the new databases, optimise them to make them more efficient and thundering fast. This will be a challenging project, but don't worry, we will be right here to provide guidance and support. So, gear up and embark on this exciting adventure with the promise of a delicious cake upon completion. Remember, the cake is a lie only if you don't put in the effort.

Options: Bachelor Thesis, Master Praktika

Teaching

Winter term 22/23:

Datenbanken

ummer term 22:

Seminar: Ausgewählte Themen des Machine Learning

Winter term 21/22:

Seminar: Ausgewählte Themen des Machine Learning

Summer term 21:

Seminar: Ausgewählte Themen des Machine Learning

Winter term 20/21:

Datenbanken

Summer term 20:

Summer term 19:

Praktikum: Natural Language Processing

Winter term 18/19:

Datenbanken

Publications

2021[ to top ]

Koopmann, T., Kobs, K., Herud, K., Hotho, A. (2021) CoBERT: Scientific Collaboration Prediction via Sequential Recommendation, in 2021 International Conference on Data Mining Workshops (ICDMW), 45–54, available: https://doi.org/10.1109/ICDMW53433.2021.00013.
- [ Abstract ]
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- [ Download ]
Collaborations are an Important factor for scientific success, as the joint work leads to results individual scientists cannot easily reach. Recommending collaborations automatically can alleviate the time consuming and tedious search for potential collaborators. Usually, such recommendation systems rely on graph structures modeling co-authorship of papers and content-based relations such as similar paper keywords. Models are then trained to estimate the probability of links between certain authors in these graphs.In this paper, we argue that the order of papers is crucial for reliably predicting future collaborations, which is not considered by graph-based recommendation systems. We thus propose to reformulate the task of collaboration recommendation as a sequential recommendation task. Here, we aim to predict the next co-author in a chronologically sorted sequence of an author’s collaborators. We introduce CoBERT, a BERT4Rec inspired model, that predicts the sequence’s next co-author and thus a potential collaborator. Since the order of co-authors of a single paper is not that important compared to the overall paper order, we leverage positional embeddings encoding paper positions instead of co-author positions in the sequence. Additionally, we inject content features about every paper and their co-authors. We evaluate CoBERT on two datasets consisting of papers from the field of Artificial Intelligence and the journal PlosOne. We show that CoBERT can outperform graph-based methods and BERT4Rec when predicting the co-authors of the next paper. We make our code and data available.

@inproceedings{koopmann2021cobert, abstract = {Collaborations are an Important factor for scientific success, as the joint work leads to results individual scientists cannot easily reach. Recommending collaborations automatically can alleviate the time consuming and tedious search for potential collaborators. Usually, such recommendation systems rely on graph structures modeling co-authorship of papers and content-based relations such as similar paper keywords. Models are then trained to estimate the probability of links between certain authors in these graphs.In this paper, we argue that the order of papers is crucial for reliably predicting future collaborations, which is not considered by graph-based recommendation systems. We thus propose to reformulate the task of collaboration recommendation as a sequential recommendation task. Here, we aim to predict the next co-author in a chronologically sorted sequence of an author’s collaborators. We introduce CoBERT, a BERT4Rec inspired model, that predicts the sequence’s next co-author and thus a potential collaborator. Since the order of co-authors of a single paper is not that important compared to the overall paper order, we leverage positional embeddings encoding paper positions instead of co-author positions in the sequence. Additionally, we inject content features about every paper and their co-authors. We evaluate CoBERT on two datasets consisting of papers from the field of Artificial Intelligence and the journal PlosOne. We show that CoBERT can outperform graph-based methods and BERT4Rec when predicting the co-authors of the next paper. We make our code and data available.}, author = {Koopmann, Tobias and Kobs, Konstantin and Herud, Konstantin and Hotho, Andreas}, booktitle = {2021 International Conference on Data Mining Workshops (ICDMW)}, keywords = {recommendation}, month = {dec}, pages = {45-54}, title = {CoBERT: Scientific Collaboration Prediction via Sequential Recommendation}, year = 2021 }
Koopmann, T., Stubbemann, M., Kapa, M., Paris, M., Buenstorf, G., Hanika, T., Hotho, A., Jäschke, R., Stumme, G. (2021) Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research, Scientometrics, available: https://doi.org/10.1007/s11192-021-03922-1.
- [ Abstract ]
- [ BibTeX ]
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Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.

@article{koopmann2021proximity, abstract = {Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.}, author = {Koopmann, Tobias and Stubbemann, Maximilian and Kapa, Matthias and Paris, Michael and Buenstorf, Guido and Hanika, Tom and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, journal = {Scientometrics}, keywords = {regio}, title = {Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research}, year = 2021 }

2020[ to top ]

Stubbemann, M., Koopmann, T. (2020) The German and International AI Network Data Set, available: https://doi.org/10.5281/zenodo.3693604.
- [ BibTeX ]
@article{koopmann-germanai_2020, author = {Stubbemann, Maximilian and Koopmann, Tobias}, keywords = {regio}, month = {mar}, publisher = {Zenodo}, title = {The German and International AI Network Data Set}, year = 2020 }
Kobs, K., Koopmann, T., Zehe, A., Fernes, D., Krop, P., Hotho, A. (2020) Where to Submit Helping Researchers to Choose the Right Venue, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, Online: Association for Computational Linguistics, 878–883, available: https://www.aclweb.org/anthology/2020.findings-emnlp.78.
- [ BibTeX ]
- [ URL ]
@inproceedings{kobs-etal-2020-submit, address = {Online}, author = {Kobs, Konstantin and Koopmann, Tobias and Zehe, Albin and Fernes, David and Krop, Philipp and Hotho, Andreas}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings}, keywords = {regio}, month = {nov}, pages = {878–883}, publisher = {Association for Computational Linguistics}, title = {Where to Submit Helping Researchers to Choose the Right Venue}, year = 2020 }

2019[ to top ]

Koopmann, T., Dallmann, A., Hettinger, L., Niebler, T., Hotho, A. (2019) On the Right Track! Analysing and Predicting Navigation Success in Wikipedia, in Proceedings of the 30th ACM Conference on Hypertext and Social Media, HT ’19, Hof, Germany: ACM, 143–152, available: https://doi.org/10.1145/3342220.3343650.
- [ BibTeX ]
- [ URL ]
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@inproceedings{Koopmann:2019:RTA:3342220.3343650, address = {New York, NY, USA}, author = {Koopmann, Tobias and Dallmann, Alexander and Hettinger, Lena and Niebler, Thomas and Hotho, Andreas}, booktitle = {Proceedings of the 30th ACM Conference on Hypertext and Social Media}, keywords = {wikipedia}, pages = {143–152}, publisher = {ACM}, series = {HT '19}, title = {On the Right Track! Analysing and Predicting Navigation Success in Wikipedia}, year = 2019 }