Data Science Chair

    Prof. Dr. Andreas Hotho

    Head of Data Science Chair and Founding Spokesman of CAIDAS 

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

    Email: hotho[at]informatik.uni-wuerzburg.de
    Phone:(+49 931) 31 - 88453
    Mobile: (+49) 173 259 40 52

    Office: Room B112 (Computer Science Building M2)
    Office Hours: By appointment only

    About me

    I am a professor at the University of Würzburg and the head of the data science chair (former DMIR group) 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.

    Research interests

    In general, my current research focus is on data science (formerly known as data mining), text mining and semantic web. 

    Meanwhile, for many years I have followed the idea to combine the processing of natural language with the explicitly represented knowledge known today as knowledge graphs. This naturally leads to research on a combination of Text Mining and NLP methods like representation learning, information extraction, metric learning and ontology learning with research on Semantic Web, or Web Science. To reach these goals, I use and adopt NLP, machine learning and data mining methods. Beside that, I also work on Sentiment Analysis, genre classification and quotation detection. I have applied these methods on historic literature, but also on Social Media data, most recently on chat messages from Twitch.tv

    Other areas I’m interested in and working on are ranking, recommendation and behavior analysis methods. Additionally, my research interests include Anomaly Detection and the analysis of Time Series mostly on the web but recently also on ERP and environmental data, for example modeling states of bee colonies by analyzing sensor data obtained from smart beehives. Since many of these problems can be approached by black box machine learning and deep learning methods, another research area of mine is on explainable AI, to gather insights and understand the models. 

    To demonstrate my results, my group is working on different application systems: BibSonomy, Everyaware and We4Bee.  


    Environmental Science

    Security and Fraud

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

    Natural Language Processing und Digital Humanities

    • Kallimachos (BMBF, 2014-2017, extended to 2019)
    • CLiGS (BMBF, 2015 -2019, extended to 2020)
    • MOTIV (bidt, 2021 - 2023)

    Recommender Systems



    • 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 )


    Selected Publications

    • 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, (P. Cellier; K. Dembczynski; A. Zimmermann; E. Devijver, Eds.) (2022).
    • InDiReCT: Language-Guided...
      InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images K. Kobs; M. Steininger; A. Hotho (2022).
    • 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).
    • 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).
    • 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.
    • iNALU: Improved Neural Ar...
      iNALU: Improved Neural Arithmetic Logic Unit D. Schlör; M. Ring; A. Hotho in Frontiers in Artificial Intelligence (2020). 3 71.
    • Emote-Controlled: Obtaini...
      Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv 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.
    • 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.