@book{chandler07semiotics,
  author = {Chandler, Daniel},
  edition = {Second},
  interhash = {c30dc178b1f00728d6a038ededaf7949},
  intrahash = {4c47ce339759c5ac03c1f4665520e788},
  publisher = {Taylor & Francis},
  title = {Semiotics: The Basics},
  year = 2007
}

@book{Mertens.1992,
  address = {Bergisch Gladbach},
  author = {Mertens, Michael},
  interhash = {9e8c68590612e355f5b05fc43d88a541},
  intrahash = {cb52418d5b4d1f4227aa409b19101567},
  isbn = {3890122981},
  publisher = {J. Eul},
  series = {Reihe Versicherungswirtschaft},
  title = {Kundentypologien im Versicherungsgeschäft mit Privatkunden},
  volume = {Bd. 10},
  year = 1992
}

@book{Peppers.2000,
  address = {London},
  author = {Peppers, Don and Rogers, Martha},
  edition = {Repr.},
  interhash = {11bfd8ca0c68a3749e753dd08214e1ca},
  intrahash = {5677ebfc7bffbb0f208c3c5981c55f00},
  isbn = {0749917121},
  publisher = {Piatkus},
  title = {Enterprise one-to-one:  Tools for building unbreakable customer relationships in the interactive age},
  year = 2000
}

@book{Tautz.2002,
  address = {Frankfurt/MainCampus Verl.},
  author = {Tautz, Frederik},
  interhash = {7f687064790307b2aa456c67c1872a1f},
  intrahash = {e7b1b6cbd67301d2b91a87d0eaf29461},
  isbn = {3593367696},
  price = {EUR 29,90},
  title = {E-Health und die Folgen:  Wie das Internet die Arzt-Patient-Beziehung und das Gesundheitssystem verändert},
  url = {http://www.gbv.de/dms/hebis-darmstadt/toc/95715339.pdf},
  year = 2002
}

@article{peters-paradigmatic-2008,
  abstract = {Classical knowledge representation methods have been successfully working for years with established – but in a way restricted and vague – relations such as synonymy, hierarchy (meronymy, hyponymy) and unspecified associations. Recent developments like ontologies and folksonomies show new forms of collaboration, indexing and knowledge representation and encourage the reconsideration of standard knowledge relationships for practical use. In a summarizing overview we show which relations are currently used in knowledge organization systems (controlled vocabularies, ontologiesand folksonomies) and which relations are expressed explicitly or which may be inherently hidden in them.},
  author = {Peters, Isabella and Weller, Katrin},
  interhash = {12015b43edecb45bae1b3e62a393340b},
  intrahash = {1b50e5ab8fc21f94bd512b012df69ec8},
  journal = {Information - Wissenschaft und Praxis},
  number = 2,
  pages = {100-107},
  title = {Paradigmatic and Syntagmatic Relations in Knowledge Organization Systems},
  url = {http://www.phil-fak.uni-duesseldorf.de/infowiss/admin/public_dateien/files/56/1204547334paradigmat.pdf},
  volume = 59,
  year = 2008
}

@inproceedings{mori2006extracting,
  abstract = {Social networks have recently garnered considerable interest. With the intention of utilizing social networks for the Semantic Web, several studies have examined automatic extraction of social networks. However, most methods have addressed extraction of the strength of relations. Our goal is extracting the underlying relations between entities that are embedded in social networks. To this end, we propose a method that automatically extracts labels that describe relations among entities. Fundamentally, the method clusters similar entity pairs according to their collective contexts in Web documents. The descriptive labels for relations are obtained from results of clustering. The proposed method is entirely unsupervised and is easily incorporated into existing social network extraction methods. Our method also contributes to ontology population by elucidating relations between instances in social networks. Our experiments conducted on entities in political social networks achieved clustering with high precision and recall. We extracted appropriate relation labels to represent the entities.},
  author = {Mori, Junichiro and Tsujishita, Takumi and Matsuo, Yutaka and Ishizuka, Mitsuru},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {International Semantic Web Conference},
  crossref = {DBLP:conf/semweb/2006},
  ee = {http://dx.doi.org/10.1007/11926078_35},
  file = {mori2006extracting.pdf:mori2006extracting.pdf:PDF},
  groups = {public},
  interhash = {457973d894180bd95e99bb6f7bb5cbc5},
  intrahash = {f1a145a60c3e4d39e91b39a7c1178110},
  pages = {487-500},
  timestamp = {2009-06-01 15:32:20},
  title = {Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts},
  username = {dbenz},
  year = 2006
}

@inproceedings{t2010kawamura,
  author = {Nagano, Shinichi and Inaba, Masumi and Kawamura, Takahiro},
  booktitle = {Proceedings of 3rd International Workshop On Social Data On The Web(SDoW2010)},
  interhash = {b12a28ca0abd62435ef43e4f166c4818},
  intrahash = {7780a712b3deb12082b9afdb1a76fa48},
  title = {Kawamura: Extracting Semantic Relations for Mining of Social Data},
  url = {http://sdow.semanticweb.org/2010/pub/sdow2010_paper_5.pdf},
  year = 2010
}

@inproceedings{trabelsi2010bridging,
  abstract = {Social book marking tools are rapidly emerging on the Web as it can be witnessed by the overwhelming number of participants. In such spaces, users annotate resources by means of any keyword or tag that they find relevant, giving raise to lightweight conceptual structures \emph{aka} folksonomies. In this respect, needless to mention that ontologies can be of benefit for enhancing information retrieval metrics. In this paper, we introduce a novel approach for ontology learning from a \textit{folksonomy}, which provide shared vocabularies and semantic relations between tags. The main thrust of the introduced approach stands in putting the focus on the discovery of \textit{non-taxonomic} relationships. The latter are often neglected, even though they are of paramount importance from a semantic point of view. The discovery process heavily relies on triadic concepts to discover and select related tags and to extract and label non-taxonomically relationships between related tags and external sources for tags filtering and non-taxonomic relationships extraction. In addition, we also discuss a new approach to evaluate obtained relations in an automatic way against WordNet repository and presents promising results for a real world \textit{folksonomy}.},
  acmid = {1934438},
  address = {Washington, DC, USA},
  author = {Trabelsi, Chiraz and Jrad, Aicha Ben and Yahia, Sadok Ben},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Data Mining Workshops},
  doi = {10.1109/ICDMW.2010.72},
  interhash = {4f2f573b32d29f76b348ee18d49c9ec4},
  intrahash = {26c469e5c064f050f35e4448d0224886},
  isbn = {978-0-7695-4257-7},
  numpages = {11},
  pages = {369--379},
  publisher = {IEEE Computer Society},
  series = {ICDMW '10},
  title = {Bridging Folksonomies and Domain Ontologies: Getting Out Non-taxonomic Relations},
  url = {http://dx.doi.org/10.1109/ICDMW.2010.72},
  year = 2010
}