Prof. Dr. Andreas Hotho

Head of the DMIR Research Group
Member of the L3S Research Center, Hanover

Julius-Maximilians-Universität Würzburg
DMIR Group, Chair VI, Computer Science
Am Hubland
D-97074 Würzburg

Raum:  B012 (Computer Science Building)
Tel:      +49 931 / 31 - 88453
Fax:     +49 931 / 31 - 86732

About me

I am a professor at the University of Würzburg and the head of the DMIR group. In this context I am directing the BibSonomy project spanning the L3S Research Center located in Hanover, the KDE group of the University of Kassel and the DMIR group.

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.

Research interests

Currently, I am working in the area of data science, data mining, semantic web mining and social media analysis.


  • 2016-2019, DFG Project, p2map: Learning Environmental Maps 
  • 2014-2017, BMBF Project, eHumanities-Center Kallimachos
  • 2011-2014, EU FET Open Project, EveryAware for enhancing environmental awareness through social information technologies
  • 2011-2016, DFG Project, Pragmatics and Semantics in Social Tagging Systems (1 + 2)
  • 2009-2015, DFG Project, Academic Publication Management (PUMA 1 + 2)





  • Best paper award: “Philipp Singer, Denis Helic, Andreas Hotho and Markus Strohmaier, HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web” at WWW Conference 2015 (link)
  • Honorable mention of the paper: “Ciro Cattuto, Dominik Benz, Andreas Hotho and Gerd Stumme, Semantic Grounding of Tag Relatedness in Social Bookmarking Systems” 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

  • p1003.pdf
    Hyptrails: A bayesian approach for comparing hypotheses about human trails. Singer, P.; Helic, D.; Hotho, A.; Strohmaier, M. (2015).
  • journal.pone.0136763.pdf
    Participatory Patterns in an International Air Quality Monitoring Initiative. Sîrbu, Alina; Becker, Martin; Caminiti, Saverio; De Baets, Bernard; Elen, Bart; Francis, Louise; Gravino, Pietro; Hotho, Andreas; Ingarra, Stefano; Loreto, Vittorio; Molino, Andrea; Mueller, Juergen; Peters, Jan; Ricchiuti, Ferdinando; Saracino, Fabio; Servedio, Vito D. P.; Stumme, Gerd; Theunis, Jan; Tria, Francesca; Van den Bossche, Joris in PLoS ONE (2015). 10(8) e0136763.
  • journal.pone.0081638.pdf
    Awareness and Learning in Participatory Noise Sensing. Becker, Martin; Caminiti, Saverio; Fiorella, Donato; Francis, Louise; Gravino, Pietro; Haklay, Mordechai (Muki); Hotho, Andreas; Loreto, Vittorio; Mueller, Juergen; Ricchiuti, Ferdinando; Servedio, Vito D. P.; Sîrbu, Alina; Tria, Francesca in PLoS ONE (2013). 8(12) e81638.
  • document.pdf
    Collective Information Extraction with Context-Specific Consistencies. Klügl, Peter; Toepfer, Martin; Lemmerich, Florian; Hotho, Andreas; Puppe, Frank in Lecture Notes in Computer Science, P. A. Flach, Bie, T. D., Cristianini, N. (eds.) (2012). (Vol. 7523) 728-743.
  • benz2010social.pdf
    The Social Bookmark and Publication Management System BibSonomy. Benz, Dominik; Hotho, Andreas; Jäschke, Robert; Krause, Beate; Mitzlaff, Folke; Schmitz, Christoph; Stumme, Gerd in The VLDB Journal (2010). 19(6) 849--875.
  • jaeschke2008tag.pdf
    Tag Recommendations in Social Bookmarking Systems. Jäschke, Robert; Marinho, Leandro; Hotho, Andreas; Schmidt-Thieme, Lars; Stumme, Gerd in AI Communications, (E. Giunchiglia, ed.) (2008). 21(4) 231-247.
  • document.pdf
    Learning Ontologies to Improve Text Clustering and Classification. Bloehdorn, Stephan; Cimiano, Philipp; Hotho, Andreas in From Data and Information Analysis to Knowledge Engineering (2006). 334--341.
  • live-1648-2403-jair.pdf
    Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis. Cimiano, Philipp; Hotho, Andreas; Staab, Steffen in Journal on Artificial Intelligence Research (2005). 24 305-339.