Tobias Koopmann, M.Sc. - Data Science Chair

Chair of Data Science (Informatik X)
University of Würzburg
Campus Hubland Nord
Emil-Fischer-Straße 50
97074 Würzburg
Germany

Email: koopmann[at]informatik.uni-wuerzburg.de

Phone: (+49 931) 31 - 89363

Office: Room 50.03.07 (Building 50)

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.

Teaching

Winter term 23/24:

Seminar: Ausgewählte Themen des Machine Learning

Winter term 22/23:

Datenbanken

Summer 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

2024[ to top ]

Völker, T., Pfister, J., Koopmann, T., and Hotho, A. (2024) “From Chat to Publication Management: Organizing your related work using BibSonomy & LLMs”, in Proceedings of the 2024 Conference on Human Information Interaction and Retrieval, CHIIR ’24, <conf-loc>, <city>Sheffield</city>, <country>United Kingdom</country>, </conf-loc>: Association for Computing Machinery, 386–390, available: https://doi.org/10.1145/3627508.3638298.
- [ Abstract ]
- [ BibTeX ]
- [ URL ]
- [ Download ]
The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications. Traditional platforms like Semantic Scholar, BibSonomy, and Zotero offer tools for literature management, but largely require manual laborious and error-prone input of tags and metadata. Here, we introduce a novel retrieval augmented generation system that leverages chat-based large language models (LLMs) to streamline and enhance the process of publication management. It provides a unified chat-based interface, enabling intuitive interactions with various backends, including Semantic Scholar, BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1) Explorative Search \& Retrieval - leveraging LLMs to search for and retrieve both specific and general scientific publications, while addressing the challenges of content hallucination and data obsolescence; and (2) Cataloguing \& Management - aiding in the organization of personal publication libraries, in this case BibSonomy, by automating the addition of metadata and tags, while facilitating manual edits and updates. We compare our system to different LLM models in three different settings, including a user study, and we can show its advantages in different metrics.

@inproceedings{10.1145/3627508.3638298, abstract = {The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications. Traditional platforms like Semantic Scholar, BibSonomy, and Zotero offer tools for literature management, but largely require manual laborious and error-prone input of tags and metadata. Here, we introduce a novel retrieval augmented generation system that leverages chat-based large language models (LLMs) to streamline and enhance the process of publication management. It provides a unified chat-based interface, enabling intuitive interactions with various backends, including Semantic Scholar, BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1) Explorative Search \& Retrieval - leveraging LLMs to search for and retrieve both specific and general scientific publications, while addressing the challenges of content hallucination and data obsolescence; and (2) Cataloguing \& Management - aiding in the organization of personal publication libraries, in this case BibSonomy, by automating the addition of metadata and tags, while facilitating manual edits and updates. We compare our system to different LLM models in three different settings, including a user study, and we can show its advantages in different metrics.}, address = {New York, NY, USA}, author = {Völker, Tom and Pfister, Jan and Koopmann, Tobias and Hotho, Andreas}, booktitle = {Proceedings of the 2024 Conference on Human Information Interaction and Retrieval}, keywords = {author:pfister}, month = {05}, pages = {386–390}, publisher = {Association for Computing Machinery}, series = {CHIIR '24}, title = {From Chat to Publication Management: Organizing your related work using BibSonomy & LLMs}, year = 2024 }
Koopmann, T., Becker, M., Lemmerich, F., and Hotho, A. (2024) “CompTrails: comparing hypotheses across behavioral networks”, Data Mining and Knowledge Discovery, available: https://doi.org/10.1007/s10618-023-00996-8.
- [ Abstract ]
- [ BibTeX ]
- [ URL ]
- [ Download ]
The term Behavioral Networks describes networks that contain relational information on human behavior. This ranges from social networks that contain friendships or cooperations between individuals, to navigational networks that contain geographical or web navigation, and many more. Understanding the forces driving behavior within these networks can be beneficial to improving the underlying network, for example, by generating new hyperlinks on websites, or by proposing new connections and friends on social networks. Previous approaches considered different hypotheses on a single network and evaluated which hypothesis fits best. These hypotheses can represent human intuition and expert opinions or be based on previous insights. In this work, we extend these approaches to enable the comparison of a single hypothesis between multiple networks. We unveil several issues of naive approaches that potentially impact comparisons and lead to undesired results. Based on these findings, we propose a framework with five flexible components that allow addressing specific analysis goals tailored to the application scenario. We show the benefits and limits of our approach by applying it to synthetic data and several real-world datasets, including web navigation, bibliometric navigation, and geographic navigation. Our work supports practitioners and researchers with the aim of understanding similarities and differences in human behavior between environments.

@article{Koopmann2024, abstract = {The term Behavioral Networks describes networks that contain relational information on human behavior. This ranges from social networks that contain friendships or cooperations between individuals, to navigational networks that contain geographical or web navigation, and many more. Understanding the forces driving behavior within these networks can be beneficial to improving the underlying network, for example, by generating new hyperlinks on websites, or by proposing new connections and friends on social networks. Previous approaches considered different hypotheses on a single network and evaluated which hypothesis fits best. These hypotheses can represent human intuition and expert opinions or be based on previous insights. In this work, we extend these approaches to enable the comparison of a single hypothesis between multiple networks. We unveil several issues of naive approaches that potentially impact comparisons and lead to undesired results. Based on these findings, we propose a framework with five flexible components that allow addressing specific analysis goals tailored to the application scenario. We show the benefits and limits of our approach by applying it to synthetic data and several real-world datasets, including web navigation, bibliometric navigation, and geographic navigation. Our work supports practitioners and researchers with the aim of understanding similarities and differences in human behavior between environments.}, author = {Koopmann, Tobias and Becker, Martin and Lemmerich, Florian and Hotho, Andreas}, journal = {Data Mining and Knowledge Discovery}, keywords = {research:recommender}, month = {01}, title = {CompTrails: comparing hypotheses across behavioral networks}, year = 2024 }

2023[ to top ]

Koopmann, T., Pfister, J., Markus, A., Carolus, A., Wienrich, C., and Hotho, A. (2023) “Higher-Order DeepTrails: Unified Approach to *Trails”, in Leyer, M. and Wichmann, J., eds., Lernen, Wissen, Daten, Analysen {(LWDA)} Conference Proceedings, Marburg, Germany, October 9-11, 2023, {CEUR} Workshop Proceedings, CEUR-WS.org, 372–386, available: https://ceur-ws.org/Vol-3630/LWDA2023-paper33.pdf.
- [ BibTeX ]
- [ URL ]
- [ Download ]
@inproceedings{DBLP:conf/lwa/Koopmann23, author = {Koopmann, Tobias and Pfister, Jan and Markus, André and Carolus, Astrid and Wienrich, Carolin and Hotho, Andreas}, booktitle = {Lernen, Wissen, Daten, Analysen {(LWDA)} Conference Proceedings, Marburg, Germany, October 9-11, 2023}, editor = {Leyer, Michael and Wichmann, Johannes}, keywords = {author:pfister}, pages = {372–386}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Higher-Order DeepTrails: Unified Approach to *Trails}, volume = 3630, year = 2023 }

2021[ to top ]

Koopmann, T., Kobs, K., Herud, K., and 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 ]
- [ BibTeX ]
- [ 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 = {selected}, month = 12, 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., and 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 ]
- [ URL ]
- [ Download ]
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. and 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 = {03}, publisher = {Zenodo}, title = {The German and International AI Network Data Set}, year = 2020 }

2019[ to top ]

Koopmann, T., Dallmann, A., Hettinger, L., Niebler, T., and 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 ]
- [ Download ]
@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 = {author:hotho}, pages = {143–152}, publisher = {ACM}, series = {HT '19}, title = {On the Right Track! Analysing and Predicting Navigation Success in Wikipedia}, year = 2019 }

Picture credits