Data Science Chair

    Prof. Dr. Andreas Hotho

    Head of Data Science Chair and Founding Spokesman of CAIDAS 
    Data Science Chair (Informatik X)
    University of Würzburg
    Campus Hubland Nord
    Emil-Fischer-Straße 50
    97074 Würzburg

    Email: hotho[at]
    Phone:(+49 931) 31 - 88453
    Mobile: (+49) 173 259 40 52
    Office: Room 50.03.004
    (Zentrum für Künstliche Intelligenz und Data Science (CAIDAS))
    Office Hours: By appointment only

    Google Scholar Profile
    DBL Profile
    Bibsonomy Profile

    I am a professor at the University of Würzburg and the head of the Data Science Chair and the founding spokesman of the Center for Artificial Intelligence and Data Science. Prior, I was a senior researcher at the University of Kassel. I started my research at the AIFB Institute at the University of Karlsruhe where I was working on text mining, ontology learning and semantic web related topics. My previous work also involved working at the KDE group of the University of Kassel on topics like data mining, semantic web mining and social media analysis. For a couple of years I've been a member of the L3S Research Center located in Hannover.

    I’m a data science expert focusing on developing new data science algorithms and machine learning models for a diverse set of applications and in several interdisciplinary collaborations, which provide interesting challenges for my research. Understanding the models by explainable AI techniques enables my group to effectively build models tailored to the specific challenges of the various application areas.

    In the past few years, applying data science and machine learning to ecosystems, environmental & climate data has become one of my central research areas. We have successfully developed deep learning methods for improving climate models in the BigData@Geo project and its successor BigData@Geo 2.0 (jointly with Heiko Paeth) as well as machine learning-based air pollution models in the EveryAware and p2Map project. We’re also analyzing data from smart beehives to understand bee behavior and detect anomalies as swarming events in the we4Bee and BeeConnected (collaboration with Ingolf Steffan-Dewenter) projects.

    Another of my major research areas is the work on LLM for Text Mining and NLP in combination with explicitly represented knowledge aka knowledge graphs. Here my group focuses on adapting LLMs and extracting or enriching them with knowledge for our applications, for example in LitBERT to learn more about characters and character networks in novels. We have already worked on methods for representation learning, information extraction, metric and ontology learning and KG enrichment for the Semantic Web and a combination of semantic representations with language models. Specifically, we are developing models for sentiment analysis, scene segmentation and relation detection. With these models, we are able to analyze the development of texts over longer periods: For example, we can follow the plot in fictional novels by tracking the detected relations between characters over scenes, or measure the development of engagement in streams on  using sentiment analysis.

    To achieve our research objectives, we’re utilizing a rich set of methodological approaches like Knowledge enriched ML, Large Language Models, Time Series and Sequence Modeling, Representation and Metric Learning and Deep Learning for Imbalanced Data, which are described in detail on my group’s research page. For a lot of our research results, we have developed and maintain tools and websites. The most known tools are Bibsonomy, a social bookmark system for publications and We4Bee, a smart beehive monitoring system. 

    In terms of scientific self-governance, I actively contribute as a PC member, reviewer, and editor across various journals, conferences, and workshops, most recently as an editor in chief for the new diamond open access journal Transactions on Graph Data and Knowledge (TGDK).


    Natural Language Processing und Digital Humanities

    • LitBERT (DFG, 2023-2026)
    • KILiMod (BMBF, 2023-2024)
    • Kallimachos (BMBF, 2014-2017, extended to 2019)
    • CLiGS (BMBF, 2015 -2019, extended to 2020)
    • MOTIV (bidt, 2021 - 2023)

    ML for Ecosystem and Climate Modeling

    Medical and Biological Data

    ML for Recommender Systems

    Security and Fraud

    • DeepScan (BMBF, 2018 -2021)
    • Promotionsförderung im Rahmen des Doktorandenprogramms des ZD.B (ZD.B Fellowships, 2017-2020)

    Physics Informed Deep Learning

    ML for Publication Data


    • Best Paper Award: "Enhancing Sequential Next-Item Prediction through Modelling Non-Item Pages" , Elisabeth Fischer, Daniel Schlör, Albin Zehe, Andreas Hotho on the Fourth International Workshop on Advanced Neural Algorithms and Theories for Recommender Systems (NeuRec) at ICDM 2023
    • Best ML Innovation Award: "Deep Learning for Climate Model Output Statistics", Michael Steininger, Daniel Abel, Katrin Ziegler, Anna Krause, Heiko Paeth, Andreas Hotho at Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020 (link)
    • Best Student Paper Award: "Evaluating the multi-task learning approach for land use regression modelling of air pollution", Andrzej Dulny, Michael Steininger, Florian Lautenschlager, Anna Krause, Andreas Hotho at FAIML 2020
    • Best Paper Award: "Financial Fraud Detection with Improved Neural Arithmetic Logic Units" by Daniel Schlör, Markus Ring, Anna Krause, Andreas Hotho on the Fifth Workshop on MIning DAta for financial applicationS Co-Hosted by ECML- PKDD 2020
    • SWSA Ten-Year Award: "Semantic Grounding of Tag Relatedness in Social Bookmarking Systems", Ciro Cattuto, Dominik Benz, Andreas Hotho, Gerd Stumme at the International Semantic Web Conference 2018 (link )
    • Best Paper Award: "HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web Philipp Singer, Denis Helic, Andreas Hotho and Markus Strohmaier,  at WWW Conference 2015 (link)
    • Honorable mention of the paper: Semantic Grounding of Tag Relatedness in Social Bookmarking Systems” Ciro Cattuto, Dominik Benz, Andreas Hotho and Gerd Stumme at ISWC 2008 (link)
    • The 7 years most influential paper award: “Information Retrieval in Folksonomies: Search and Ranking”, Andreas Hotho, Robert Jäschke, Christoph Schmitz, Gerd Stumme at ESWC 2013 (link )

    Current activities:

    Past activities:

    • ConvMOS: climate model ou...
      ConvMOS: climate model output statistics with deep learning M. Steininger; D. Abel; K. Ziegler; A. Krause; H. Paeth; A. Hotho in Data Mining and Knowledge Discovery (2023). 37(1) 136–166.
    • InDiReCT: Language-Guided...
      InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images K. Kobs; M. Steininger; A. Hotho (2022).
    • Detecting Scenes in Ficti...
      Detecting Scenes in Fiction: A new Segmentation Task A. Zehe; L. Konle; L. Dümpelmann; E. Gius; A. Hotho; F. Jannidis; L. Kaufmann; M. Krug; F. Puppe; N. Reiter; A. Schreiber; N. Wiedmer (2021).
    • Density-based weighting f...
      Density-based weighting for imbalanced regression M. Steininger; K. Kobs; P. Davidson; A. Krause; A. Hotho in Machine Learning, (A. Appice; S. Escalera; J. A. Gamez; H. Trautmann, Eds.) (2021).
    • Emote-Controlled: Obtaini...
      Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Channels K. Kobs; A. Zehe; A. Bernstetter; J. Chibane; J. Pfister; J. Tritscher; A. Hotho in {ACM} Transactions on Social Computing (2020). 3(2) 1–34.
    • iNALU: Improved Neural Ar...
      iNALU: Improved Neural Arithmetic Logic Unit D. Schlör; M. Ring; A. Hotho in Frontiers in Artificial Intelligence (2020). 3 71.
    • LM4KG: Improving Common S...
      LM4KG: Improving Common Sense Knowledge Graphs with Language Models J. Omeliyanenko; A. Zehe; L. Hettinger; A. Hotho J. Z. Pan, V. Tamma, C. d’Amato, K. Janowicz, B. Fu, A. Polleres, O. Seneviratne, L. Kagal (Eds.) (2020). 456–473.
    • Participatory Patterns in...
      Participatory Patterns in an International Air Quality Monitoring Initiative A. Sîrbu; M. Becker; S. Caminiti; B. De Baets; B. Elen; L. Francis; P. Gravino; A. Hotho; S. Ingarra; V. Loreto; A. Molino; J. Mueller; J. Peters; F. Ricchiuti; F. Saracino; V. D. P. Servedio; G. Stumme; J. Theunis; F. Tria; J. Van den Bossche in PLoS ONE (2015). 10(8) e0136763.
    • Hyptrails: A bayesian app...
      Hyptrails: A bayesian approach for comparing hypotheses about human trails P. Singer; D. Helic; A. Hotho; M. Strohmaier (2015).
    • Awareness and Learning in...
      Awareness and Learning in Participatory Noise Sensing M. Becker; S. Caminiti; D. Fiorella; L. Francis; P. Gravino; M. (Muki) Haklay; A. Hotho; V. Loreto; J. Mueller; F. Ricchiuti; V. D. P. Servedio; A. Sîrbu; F. Tria in PLoS ONE (2013). 8(12) e81638.
    • Collective Information Ex...
      Collective Information Extraction with Context-Specific Consistencies. P. Klügl; M. Toepfer; F. Lemmerich; A. Hotho; F. Puppe in Lecture Notes in Computer Science, P. A. Flach, T. D. Bie, N. Cristianini (Eds.) (2012). (Vol. 7523) 728–743.
    • The Social Bookmark and P...
      The Social Bookmark and Publication Management System BibSonomy D. Benz; A. Hotho; R. Jäschke; B. Krause; F. Mitzlaff; C. Schmitz; G. Stumme in The VLDB Journal (2010). 19(6) 849–875.
    • Tag Recommendations in So...
      Tag Recommendations in Social Bookmarking Systems R. Jäschke; L. Marinho; A. Hotho; L. Schmidt-Thieme; G. Stumme in AI Communications, (E. Giunchiglia, Ed.) (2008). 21(4) 231–247.
    • Learning Ontologies to Im...
      Learning Ontologies to Improve Text Clustering and Classification S. Bloehdorn; P. Cimiano; A. Hotho in From Data and Information Analysis to Knowledge Engineering (2006). 334–341.
    • Learning Concept Hierarch...
      Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis P. Cimiano; A. Hotho; S. Staab in Journal on Artificial Intelligence Research (2005). 24 305–339.