@article{landia2013deeper,
  abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.},
  author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan},
  interhash = {e8095b13630452ce3ecbae582f32f4bc},
  intrahash = {e585a92994be476480545eb62d741642},
  journal = {cs.IR},
  title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
  url = {http://arxiv.org/abs/1310.1498},
  volume = {1310.1498},
  year = 2013
}

@article{landia2013deeper,
  abstract = {The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.},
  author = {Landia, Nikolas and Doerfel, Stephan and Jäschke, Robert and Anand, Sarabjot Singh and Hotho, Andreas and Griffiths, Nathan},
  interhash = {e8095b13630452ce3ecbae582f32f4bc},
  intrahash = {e585a92994be476480545eb62d741642},
  journal = {cs.IR},
  title = {Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations},
  url = {http://arxiv.org/abs/1310.1498},
  volume = {1310.1498},
  year = 2013
}

@inproceedings{schmitz2006content,
  abstract = {Recently, research projects such as PADLR and SWAP
  have developed tools like Edutella or Bibster, which are targeted at
  establishing peer-to-peer knowledge management (P2PKM) systems.  In
  such a system, it is necessary to obtain provide brief semantic
  descriptions of peers, so that routing algorithms or matchmaking
  processes can make decisions about which communities peers should
  belong to, or to which peers a given query should be forwarded.

  This paper provides a graph clustering technique on
  knowledge bases for that purpose. Using this clustering, we can show
  that our strategy requires up to 58% fewer queries than the
  baselines to yield full recall in a bibliographic P2PKM scenario.},
  address = {Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {The Semantic Web: Research and Applications},
  editor = {Sure, York and Domingue, John},
  interhash = {d2ddbb8f90cd271dc18670e4c940ccfb},
  intrahash = {1788c88e04112a4491f19dfffb8dc39e},
  pages = {530-544},
  publisher = {Springer},
  series = {LNAI},
  title = {Content Aggregation on Knowledge Bases using Graph Clustering},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf},
  volume = 4011,
  year = 2006
}

@inproceedings{mitzlaff2010visit,
  address = {Toronto, Canada},
  author = {Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Proceedings of the 21st ACM conference on Hypertext and hypermedia},
  file = {mitzlaff2010visit.pdf:mitzlaff2010visit.pdf:PDF},
  interhash = {5584c4c57fcd8eb4663df8b114bcf09c},
  intrahash = {a97c4f7e80dcb666450acf697002155e},
  note = {(to appear)},
  title = {Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy},
  url = {http://www.kde.cs.uni-kassel.de/pub/pdf/mitzlaff2010visit.pdf},
  year = 2010
}

@inproceedings{mitzlaff2010visit,
  address = {Toronto, Canada},
  author = {Mitzlaff, Folke and Benz, Dominik and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Proceedings of the 21st ACM conference on Hypertext and hypermedia},
  file = {mitzlaff2010visit.pdf:mitzlaff2010visit.pdf:PDF},
  interhash = {5584c4c57fcd8eb4663df8b114bcf09c},
  intrahash = {a97c4f7e80dcb666450acf697002155e},
  note = {(to appear)},
  title = {Visit me, click me, be my friend: An analysis of evidence networks of user relationships in Bibsonomy},
  url = {http://www.kde.cs.uni-kassel.de/pub/pdf/mitzlaff2010visit.pdf},
  year = 2010
}

@inproceedings{eklund00contextual,
  address = {Heidelberg},
  author = {Eklund, P. and Groh, B. and Stumme, G. and Wille, R.},
  booktitle = {Conceptual Structures: Logical, Linguistic, and Computational},
  comment = {alpha},
  editor = {Ganter, B. and Mineau, G. W.},
  interhash = {b15b76a5407efbdbb2a11b18a13febcb},
  intrahash = {eb6e678cba7f48ae8604d59542cd79c6},
  pages = {453-467},
  publisher = {Springer},
  series = {LNAI},
  title = {Contextual-Logic Extension of TOSCANA.},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2000/ICCS_toscanaextension.pdf},
  volume = 1867,
  year = 2000
}

@inproceedings{stumme95geometrical,
  address = {Heidelberg},
  author = {Stumme, Gerd and Wille, Rudolf},
  booktitle = {Graph Drawing},
  comment = {alpha},
  editor = {Tamassia, R. and Tollis, I.G.},
  interhash = {0e3ac1bae7ef38507b1ff5b5bc6c4d49},
  intrahash = {069db3a0aad592c82f35e1bbf701824f},
  pages = {452-459},
  publisher = {Springer},
  series = {LNCS},
  title = {A Geometrical Heuristic for Drawing Concept Lattices},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/1994/P1677-GD94.pdf},
  volume = 894,
  year = 1995
}

@inproceedings{schmitz2006content,
  abstract = {Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded.

This paper provides a graph clustering technique on knowledge bases for that purpose. Using this clustering, we can show that our strategy requires up to 58% fewer queries than the baselines to yield full recall in a bibliographic P2PKM scenario.},
  address = {Berlin/Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {The Semantic Web: Research and Applications},
  doi = {10.1007/11762256_39},
  editor = {Sure, York and Domingue, John},
  interhash = {d2ddbb8f90cd271dc18670e4c940ccfb},
  intrahash = {1788c88e04112a4491f19dfffb8dc39e},
  isbn = {978-3-540-34544-2},
  issn = {0302-9743},
  month = jun,
  pages = {530--544},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Content Aggregation on Knowledge Bases using Graph Clustering},
  url = {http://www.springerlink.com/content/u121v1827v286398/},
  volume = 4011,
  year = 2006
}

@inproceedings{schmitz2006content,
  address = {Budva, Montenegro},
  author = {Schmitz, Christoph and Hotho, Andreas and J\"aschke, Robert and Stumme, Gerd},
  booktitle = {Proceedings of the 3rd European Semantic Web Conference},
  interhash = {940fa3c671c771cc9a644b3ecfef43cd},
  intrahash = {9a06428ec3bd72e3ea6c7a8f08e2bb85},
  isbn = {3-540-34544-2},
  month = {June},
  pages = {530-544},
  publisher = {Springer},
  series = {LNCS},
  title = {Content Aggregation on Knowledge Bases using Graph Clustering},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/schmitz2006sumarize_eswc.pdf},
  vgwort = {27},
  volume = 4011,
  year = 2006
}

@inproceedings{hotho2006information,
  abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.},
  address = {Heidelberg},
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {The Semantic Web: Research and Applications},
  editor = {Sure, York and Domingue, John},
  interhash = {10ec64d80b0ac085328a953bb494fb89},
  intrahash = {3c301945817681d637ee43901c016939},
  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{schmitz2006content,
  abstract = {Recently, research projects such as PADLR and SWAP
  have developed tools like Edutella or Bibster, which are targeted at
  establishing peer-to-peer knowledge management (P2PKM) systems.  In
  such a system, it is necessary to obtain provide brief semantic
  descriptions of peers, so that routing algorithms or matchmaking
  processes can make decisions about which communities peers should
  belong to, or to which peers a given query should be forwarded.

  This paper provides a graph clustering technique on
  knowledge bases for that purpose. Using this clustering, we can show
  that our strategy requires up to 58% fewer queries than the
  baselines to yield full recall in a bibliographic P2PKM scenario.},
  address = {Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {The Semantic Web: Research and Applications},
  editor = {Sure, York and Domingue, John},
  interhash = {d2ddbb8f90cd271dc18670e4c940ccfb},
  intrahash = {1788c88e04112a4491f19dfffb8dc39e},
  pages = {530-544},
  publisher = {Springer},
  series = {LNAI},
  title = {Content Aggregation on Knowledge Bases using Graph Clustering},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006content.pdf},
  volume = 4011,
  year = 2006
}