@phdthesis{peters2009folksonomies,
  author = {Peters, Isabella},
  interhash = {f52c87372515e42e0cde602a2fe8da39},
  intrahash = {a25702677dc406b1be7878215277050c},
  school = {Universität Düsseldorf},
  title = {Folksonomies in Wissensrepräsentation und Information Retrieval},
  type = {PhD thesis},
  year = 2009
}

@article{stock2008folksonomies,
  abstract = {Folksonomies in Wissensrepräsentation und Information Retrieval.
Die populären Web 2.0-Dienste werden von Prosumern – Produzenten und gleichsam Konsumenten – nicht nur dazu genutzt, Inhalte zu produzieren, sondern auch, um sie inhaltlich zu erschließen. Folksonomies erlauben es dem Nutzer, Dokumente mit eigenen Schlagworten, sog. Tags, zu beschreiben, ohne dabei auf gewisse Regeln oder Vorgaben achten zu müssen. Neben einigen Vorteilen zeigen Folksonomies aber auch zahlreiche Schwächen (u. a. einen Mangel an Präzision). Um diesen Nachteilen größtenteils entgegenzuwirken, schlagen wir eine Interpretation der Tags als natürlichsprachige Wörter vor. Dadurch ist es uns möglich, Methoden des Natural Language Processing (NLP) auf die Tags anzuwenden und so linguistische Probleme der Tags zu beseitigen. Darüber hinaus diskutieren wir Ansätze und weitere Vorschläge (Tagverteilungen, Kollaboration und akteurspezifische Aspekte) hinsichtlich eines Relevance Rankings von getaggten Dokumenten. Neben Vorschlägen auf ähnliche Dokumente („more like this!“) erlauben Folksonomies auch Hinweise auf verwandte Nutzer und damit auf Communities („more like me!“).

Folksonomies in Knowledge Representation and Information Retrieval
In Web 2.0 services “prosumers” – producers and consumers – collaborate not only for the purpose of creating content, but to index these pieces of information as well. Folksonomies permit actors to describe documents with subject headings, “tags“, without regarding any rules. Apart from a lot of benefits folksonomies have many shortcomings (e.g., lack of precision). In order to solve some of the problems we propose interpreting tags as natural language terms. Accordingly, we can introduce methods of NLP to solve the tags’ linguistic problems. Additionally, we present criteria for tagged documents to create a ranking by relevance (tag distribution, collaboration and actor-based aspects). Besides recommending similar documents („more like this!“) folksonomies can be used for the recommendation of similar users and communities („more like me!“).},
  author = {Peters, Isabella and Stock, Wolfgang G.},
  interhash = {a9a40e30db7636b46aca513bb73c6689},
  intrahash = {16fa5c5fc155e296ff885bf62d1a1230},
  journal = {Information - Wissenschaft und Praxis},
  number = 2,
  pages = {77--90},
  title = {Folksonomies in Wissensrepräsentation und Information Retrieval},
  url = {http://www.phil-fak.uni-duesseldorf.de/infowiss/mitarbeiter/wissenschaftliche-mitarbeiter-hilfskraefte/isabella-peters/012-folksonomies-in-wissensrepraesentation-und-information-retrieval/},
  volume = 59,
  year = 2008
}

@article{davis1993knowledge,
  abstract = {Although knowledge representation is one of the central and in some ways most familiar concepts in AI, the most fundamental question about it--What is it?--has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, while still others have focused on properties that are important to the notion of representation in general.

In this paper we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and at times conflicting demands on the properties a representation should have.

We argue that keeping in mind all five of these roles provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field. },
  author = {Davis, Randall and Shrobe, Howard and Szolovits, Peter},
  interhash = {0a9d5e8f1265106c18730053f871e80b},
  intrahash = {fc0910c9b3d967f5b01ae73d252d66fb},
  journal = {AI Magazine},
  number = 1,
  pages = {17--33},
  title = {What is a Knowledge Representation},
  url = {http://www.aaai.org/aitopics/assets/PDF/AIMag14-01-002.pdf},
  volume = 14,
  year = 1993
}