@inproceedings{singer2015hyptrails,
  address = {Firenze, Italy},
  author = {Singer, P. and Helic, D. and Hotho, A. and Strohmaier, M.},
  booktitle = {24th International World Wide Web Conference (WWW2015)},
  interhash = {d33e150aa37dcd618388960286f8a46a},
  intrahash = {5d21e53dc91b35a4a6cb6b9ec858045d},
  month = {May 18 - May 22},
  organization = {ACM},
  publisher = {ACM},
  title = {Hyptrails: A bayesian approach for comparing hypotheses about human trails},
  url = {http://www.www2015.it/documents/proceedings/proceedings/p1003.pdf},
  year = 2015
}

@book{balbymarinho2012recommender,
  abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.},
  author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
  doi = {10.1007/978-1-4614-1894-8},
  interhash = {0bb7f0588cd690d67cc73e219a3a24fa},
  intrahash = {87d6883ebd98e8810be45d7e7e4ade96},
  isbn = {978-1-4614-1893-1},
  month = feb,
  publisher = {Springer},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  title = {Recommender Systems for Social Tagging Systems},
  url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
  year = 2012
}

@article{benz2010social,
  abstract = {Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.},
  author = {Benz, D and Hotho, A and Jaschke, R and Krause, B and Mitzlaff, F and Schmitz, C and Stumme, G},
  interhash = {102300e311e97ef3f7d78c64c347bf14},
  intrahash = {3df3df75c079a4b2aa5535048fa59d7f},
  journal = {VLDB JOURNAL},
  month = {12},
  number = 6,
  title = {The social bookmark and publication management system bibsonomy A platform for evaluating and demonstrating Web 2.0 research},
  uniqueid = {000286037700006|edswsc},
  volume = 19,
  year = 2010
}

@article{benz2010academic,
  author = {Benz, D. and Hotho, A. and Jaschke, R. and Stumme, G. and Halle, A. and Lima, A. G. S. and Steenweg, H. and Stefani, S. and Lalmas, M. and Jose, J. and Rauber, A. and Sebastiani, F. and Frommholz, I.},
  interhash = {82f58238df3f0f36327daf86b54128cb},
  intrahash = {da1a070510ce488d2f5c75fb7d80b31d},
  journal = {LECTURE NOTES IN COMPUTER SCIENCE},
  month = {1},
  number = 6273,
  title = {Academic Publication Management with PUMA - Collect, Organize and Share Publications},
  uniqueid = {RN278581523|edsbl},
  year = 2010
}

@article{benz2010academic,
  author = {Benz, D. and Hotho, A. and Jaschke, R. and Stumme, G. and Halle, A. and Lima, A. G. S. and Steenweg, H. and Stefani, S. and Lalmas, M. and Jose, J. and Rauber, A. and Sebastiani, F. and Frommholz, I.},
  interhash = {82f58238df3f0f36327daf86b54128cb},
  intrahash = {da1a070510ce488d2f5c75fb7d80b31d},
  journal = {LECTURE NOTES IN COMPUTER SCIENCE},
  month = {1},
  number = 6273,
  title = {Academic Publication Management with PUMA - Collect, Organize and Share Publications},
  uniqueid = {RN278581523|edsbl},
  year = 2010
}

@book{balbymarinho2012recommender,
  abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.},
  author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
  doi = {10.1007/978-1-4614-1894-8},
  interhash = {0bb7f0588cd690d67cc73e219a3a24fa},
  intrahash = {87d6883ebd98e8810be45d7e7e4ade96},
  isbn = {978-1-4614-1893-1},
  month = feb,
  publisher = {Springer},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  title = {Recommender Systems for Social Tagging Systems},
  url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
  year = 2012
}

@book{balbymarinho2012recommender,
  abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.},
  author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
  interhash = {0bb7f0588cd690d67cc73e219a3a24fa},
  intrahash = {87d6883ebd98e8810be45d7e7e4ade96},
  isbn = {978-1-4614-1893-1},
  month = feb,
  publisher = {Springer},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  title = {Recommender Systems for Social Tagging Systems},
  url = {http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-4614-1893-1},
  year = 2012
}

@book{balbymarinho2012recommender,
  abstract = {Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.},
  author = {Balby Marinho, L. and Hotho, A. and Jäschke, R. and Nanopoulos, A. and Rendle, S. and Schmidt-Thieme, L. and Stumme, G. and Symeonidis, P.},
  doi = {10.1007/978-1-4614-1894-8},
  interhash = {0bb7f0588cd690d67cc73e219a3a24fa},
  intrahash = {87d6883ebd98e8810be45d7e7e4ade96},
  isbn = {978-1-4614-1893-1},
  month = feb,
  publisher = {Springer},
  series = {SpringerBriefs in Electrical and Computer Engineering},
  title = {Recommender Systems for Social Tagging Systems},
  url = {http://link.springer.com/book/10.1007/978-1-4614-1894-8},
  year = 2012
}

@article{cimiano_hotho_staab_2005,
  author = {Cimiano, P. and Hotho, A. and Staab, S.},
  interhash = {4c09568cff62babd362aab03095f4589},
  intrahash = {8299d264161ecd740168c89b781f84ae},
  journal = {Journal of Artificial Intelligence Research},
  number = 1,
  pages = {305-339},
  title = {Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis},
  url = {http://ontology.csse.uwa.edu.au/reference/browse_paper.php?pid=233281549},
  volume = 24,
  year = 2005
}

@article{cattuto2007,
  author = {Cattuto, C. and Schmitz, C. and Baldassarri, A. and Servedio, V. D. P. and Loreto, V. and Hotho, A. and Grahl, M. and Stumme, G.},
  interhash = {fc5f2df61d28bc99b7e15029da125588},
  intrahash = {d87e198a6d564ae8a8fe151e0a96fa0f},
  journal = {AI Communications},
  number = 4,
  pages = {245 - 262},
  title = {Network Properties of Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2007/aicomm_2007_folksonomy_clustering.pdf},
  vgwort = {67},
  volume = 20,
  year = 2007
}

@inproceedings{stumme02usage,
  address = {Baltimore},
  author = {Stumme, G. and Berendt, B. and Hotho, A.},
  booktitle = {Proc. NSF Workshop on Next Generation Data Mining},
  comment = {alpha},
  interhash = {479de77764be1ec66534be1c647e0857},
  intrahash = {4a68d1443065dcd7980989e97cb0af69},
  month = {November},
  pages = {77-86},
  privnote = {alpha},
  title = {Usage Mining for and on the Semantic Web},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/NSF-NGDM02.pdf},
  year = 2002
}

@inproceedings{hotho02conceptualclustering,
  author = {Hotho, A. and Stumme, G.},
  booktitle = {Proc. Fachgruppentreffen Maschinelles Lernen (FGML 2002)},
  comment = {alpha},
  editor = {K\'okai, G. and Zeidler, J.},
  interhash = {3dd3d4ce38d0de0ba8e167f8133cbb3e},
  intrahash = {e253c44552a046fe90236274bcfeab13},
  pages = {37-45},
  privnote = {alpha},
  title = {Conceptual Clustering of Text Clusters},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/FGML02.pdf},
  year = 2002
}

@inproceedings{hotho03wordnet,
  address = {Toronto},
  author = {Hotho, A and Staab, S. and Stumme, G.},
  booktitle = {Proc. SIGIR Semantic Web Workshop},
  comment = {alpha},
  interhash = {c2a9a89ce20cef90a1e78d34dc2c2afe},
  intrahash = {04c7d86337d68e4ed9ae637029c43414},
  privnote = {alpha},
  title = {Wordnet improves text document clustering},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003wordnet.pdf},
  year = 2003
}

@inproceedings{berendt02towards,
  address = {Heidelberg},
  author = {Berendt, B. and Hotho, A. and Stumme, G.},
  booktitle = {The Semantic Web -- ISWC 2002},
  comment = {alpha},
  editor = {Horrocks, I. and Hendler, J.},
  interhash = {4dd40c50089d3b86fb235bfaf3c8bee7},
  intrahash = {fc1c88be5f8c2640ca6e9a40b5fa1c7b},
  pages = {264-278},
  privnote = {alpha},
  publisher = {Springer},
  series = {LNCS},
  title = {Towards Semantic Web Mining},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/ISWC02.pdf},
  year = 2002
}

@inproceedings{hartmann02semanticweb,
  address = {Oldenburg},
  author = {Hartmann, J. and Hotho, A. and Stumme, G.},
  booktitle = {Proc. Arbeitskreistreffen Knowledge Discovery},
  comment = {alpha},
  interhash = {c07545febc9e7b32803bf33547ec9004},
  intrahash = {a5f1a8b42409b96271bc5c671deceea9},
  month = {September},
  privnote = {alpha},
  title = {Semantic Web Mining for Building Information Portals (Position Paper)},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2002/hartmann2002semanticweb.pdf},
  year = 2002
}

@inproceedings{hotho2006information,
  address = {Heidelberg},
  author = {Hotho, A. and Jaeschke, R. and Schmitz, C. and Stumme, G.},
  booktitle = {The Semantic Web: Research and Applications},
  interhash = {ecd1b171520f24c97c5dd3c41ad2164a},
  intrahash = {f4ddfeef3baf5513258c48271f5d7fb7},
  month = {June},
  pages = {411-426},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Information Retrieval in Folksonomies: Search and Ranking},
  volume = 4011,
  year = 2006
}

@inproceedings{WaScHoP07,
  address = {Alexandria (Virginia)},
  author = {Wagner, R. and Scholz, S. and Hotho, A.},
  booktitle = {{P}roceedings of the 2007 {E}uropean {C}ompetitive {I}ntelligence {S}ummit},
  editor = {Michaeli, R.},
  interhash = {3fbbbdb07c2049630f86ef71cce252bb},
  intrahash = {f15fc400b0577b48801606e08ca22be8},
  organization = {Society of Competitive Intelligence Professionals},
  title = {{T}he {D}ark {S}ide of {C}ompetitive {I}ntelligence: {T}echnocrats at {W}ork},
  year = 2007
}

@article{346336,
  address = {New York, NY, USA},
  author = {Staab, S. and Angele, J. and Decker, S. and Erdmann, M. and Hotho, A. and Maedche, A. and Schnurr, H.-P. and Studer, R. and Sure, Y.},
  doi = {http://dx.doi.org/10.1016/S1389-1286(00)00039-6},
  interhash = {93e9f10176d9f06be1658ff793f7c2ea},
  intrahash = {46b6d5dd3788371c719decd0c2d4897e},
  issn = {1389-1286},
  journal = {Comput. Netw.},
  number = {1-6},
  pages = {473--491},
  publisher = {Elsevier North-Holland, Inc.},
  title = {Semantic community Web portals},
  url = {http://portal.acm.org/citation.cfm?id=346241.346336&coll=GUIDE&dl=GUIDE&CFID=7705918&CFTOKEN=32369470},
  volume = 33,
  year = 2000
}

@inproceedings{hotho_fgml02,
  author = {Hotho, A. and Stumme, G.},
  booktitle = {Proceedings of FGML Workshop},
  interhash = {3dd3d4ce38d0de0ba8e167f8133cbb3e},
  intrahash = {18fdbebb76d48feccf2dceed23f4cd74},
  pages = {37-45},
  publisher = {Special Interest Group of German Informatics Society (FGML ---              Fachgruppe Maschinelles Lernen der GI e.V.)},
  title = {Conceptual Clustering of Text Clusters},
  url = {\url{http://www.aifb.uni-karlsruhe.de/WBS/aho/pub/tc_fca_2002_submit.pdf}},
  year = 2002
}

@inproceedings{hotho_pkdd03,
  author = {Hotho, A. and Staab, S. and Stumme, G.},
  booktitle = {Proc. of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD},
  interhash = {cf66183151a5d94a0941ac6d5089ae89},
  intrahash = {c1bb26aa5d4801542f832ffab70c82e5},
  pages = {217-228},
  series = {LNCS},
  title = {Explaining Text Clustering Results using Semantic Structures},
  volume = 2838,
  year = 2003
}