@inproceedings{christiaens2006metadata,
  abstract = {In this paper we give a brief overview of different metadata mechanisms (like ontologies and folksonomies) and how they relate to each other. We identify major strengths and weaknesses of these mechanisms. We claim that these mechanisms can be classified from restricted (e.g., ontology) to free (e.g., free text tagging). In our view, these mechanisms should not be used in isolation, but rather as complementary solutions, in a continuous process wherein the strong points of one increase the semantic depth of the other. We give an overview of early active research already going on in this direction and propose that methodologies to support this process be developed. We demonstrate a possible approach, in which we mix tagging, taxonomy and ontology.},
  author = {Christiaens, Stijn},
  booktitle = {Lecture Notes in Computer Science: On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops},
  file = {christiaens2006metadata.pdf:christiaens2006metadata.pdf:PDF},
  groups = {public},
  interhash = {f733d993459329ed1ef9f26d303ba0d9},
  intrahash = {efc1396e845f3db1688dc8ef154d9520},
  lastdatemodified = {2007-01-04},
  lastname = {Christiaens},
  own = {notown},
  pdf = {christiaens06-metadata.pdf},
  publisher = {Springer},
  read = {notread},
  timestamp = {2007-09-11 13:31:23},
  title = {Metadata Mechanisms: From Ontology to Folksonomy ... and Back},
  url = {http://www.springerlink.com/content/m370107220473394},
  username = {dbenz},
  workshoppub = {1},
  year = 2006
}

@inproceedings{kim2006text,
  abstract = {This paper presents a series of text-mining algorithms for managing knowledge directory, which is one of the most crucial problems in constructing knowledge management systems today. In future systems, the constructed directory, in which knowledge objects are automatically classified, should evolve so as to provide a good indexing service, as the knowledge collection grows or its usage changes. One challenging issue is how to combine manual and automatic organization facilities that enable a user to flexibly organize obtained knowledge by the hierarchical structure over time. To this end, I propose three algorithms that utilize text mining technologies: semi-supervised classification, semi-supervised clustering, and automatic directory building. Through experiments using controlled document collections, the proposed approach is shown to significantly support hierarchical organization of large electronic knowledge base with minimal human effort},
  address = {Berlin, Germany},
  author = {Kim, Han-Joon},
  booktitle = {Proceedings of the First International Conference on Knowledge Science, Engineering and Management (KSEM'06)},
  dateadded = {2006-09-30},
  editor = {Lang, J and Lin, F and Wang, J.},
  file = {kim2006text.pdf:kim2006text.pdf:PDF},
  groups = {public},
  interhash = {cd7783b34b37d402830ac0f4477a3c44},
  intrahash = {babcd6f6b809e6727a8e7a0d188b4325},
  lastdatemodified = {2006-09-30},
  lastname = {Han-joon},
  month = {August},
  own = {notown},
  pages = {202-214},
  pdf = {kim06-text.pdf},
  publisher = {Springer},
  read = {notread},
  series = {Lecture Notes in Artificial Intelligence},
  timestamp = {2007-09-11 13:31:16},
  title = {On Text Mining Algorithms for Automated Maintenance of Hierarchical Knowledge Directory},
  url = {http://dx.doi.org/10.1007/11811220_18},
  username = {dbenz},
  volume = 4092,
  year = 2006
}

@inproceedings{schmitz2006mining,
  abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.},
  address = {Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {Data Science and Classification. Proceedings of the 10th IFCS Conf.},
  editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and �iberna, A.},
  file = {schmitz2006mining.pdf:schmitz2006mining.pdf:PDF},
  groups = {public},
  interhash = {9f407e0b779aba5b3afca7fb906f579b},
  intrahash = {ed504c16bc4eb561a9446bd98b10dca1},
  lastdatemodified = {2006-12-07},
  lastname = {Schmitz},
  month = {July},
  own = {notown},
  pages = {261--270},
  pdf = {schmitz06-mining.pdf},
  publisher = {Springer},
  read = {notread},
  series = {Studies in Classification, Data Analysis, and Knowledge Organization},
  timestamp = {2007-09-11 13:31:35},
  title = {Mining Association Rules in Folksonomies},
  username = {dbenz},
  year = 2006
}

@inproceedings{hotho06-information,
  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.},
  ad_pdf = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/seach2006hotho_eswc.pdf},
  address = {Heidelberg},
  author = {Hotho, Andreas and J�schke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {The Semantic Web: Research and Applications},
  editor = {Sure, York and Domingue, John},
  interhash = {aa0a40dd836bfde8397409adfdc4a3f2},
  intrahash = {b1e4dabc5b558aeea1b839a7f123eef1},
  lastdatemodified = {2006-07-18},
  lastname = {Hotho},
  month = {June},
  own = {own},
  pages = {411-426},
  pdf = {hotho06-information.pdf},
  publisher = {Springer},
  read = {read},
  series = {LNAI},
  title = {Information Retrieval in Folksonomies: Search and Ranking},
  url = {http://.kde.cs.uni-kassel.de/hotho},
  volume = 4011,
  year = 2006
}

@inproceedings{michlmayr2005case,
  abstract = {This paper delivers a case study on the properties of meta- data provided by a folksonomy. We provide the background about folk- sonomies and discuss to which extend the process of creating meta-data in a folksonomy is related to the idea of emergent semantics as defined by the IFIP 2.6 Working Group on Data Semantics. We conduct exper- iments to analyse the meta-data provided by the del.icio.us folksonomy and to develop a method for selecting subsets of meta-data that adhere to the principle of interest-based locality, which was originally observed in peer-to-peer environments. In addition, we compare data provided by del.icio.us to data provided by the DMOZ taxonomy.},
  author = {Michlmayr, Elke},
  booktitle = {Proceedings of the Workshop on Social Network Analysis, International Semantic Web Conference (ISWC)},
  file = {:michlmayr05-emergent.pdf:PDF},
  groups = {public},
  interhash = {9aa76dc961b982569554554fa3ef5de9},
  intrahash = {8799bc711fb192a577791a5fdea805f0},
  lastdatemodified = {2007-04-27},
  lastname = {Michlmayr},
  month = {November},
  own = {notown},
  pdf = {michlmayr05-emergent.pdf},
  read = {notread},
  timestamp = {2009-11-11 16:55:59},
  title = {A Case Study on Emergent Semantics in Communities},
  url = {http://wit.tuwien.ac.at/people/michlmayr/index.html},
  username = {dbenz},
  year = 2005
}

@mastersthesis{albrecht2006folksonomy,
  abstract = {Folksonomy ist eine neuartige Form des Datenmanagements, die auf dem Vorgang des Taggings basiert. Durch die sich immer st�rker ausbreitende Vernetzung werden neue Arten der Organisation der dynamischen Inhalte des Webs notwendig. Folksonomy bietet diese neue Denkweise, die von herk�mmlichen Ans�tzen, wie Taxonomien und Ontologien, vollst�ndig abkommt. Die Daten werden mit Tags belegt anstatt sie hierarchisch zu strukturieren. Durch das Fehlen dieser hierarchischen Abh�ngigkeiten sind Folksonomien weitaus flexibler und dynamischer als starre klassifizierungssysteme und sind den gesteigerten Anforderungen des Webs gewachsen. In dieser Arbeit wird auf die Unterschiede zwischen herk�mmlichen hierarchischen Organisationsmodellen und Folksonomien eingegangen und ein �berblick �ber das Thema Folksonomy gegeben.},
  address = {Wien},
  author = {Albrecht, Christine},
  file = {albrecht2006folksonomy.pdf:albrecht2006folksonomy.pdf:PDF},
  interhash = {fcb1b556fc13e3f7529d2f648bddb135},
  intrahash = {f52dcbeda8dda19e82280749523f4a85},
  lastname = {Albrecht},
  misc = {lastdatemodified = 2006-12-04},
  month = {March},
  note = {Diplomarbeit am Institut f�r Gestaltungs- und Wirkungsforschung, TU Wien},
  own = {own},
  pdf = {albrecht06-folksonomy.pdf},
  read = {readnext},
  school = {TU Wien},
  title = {Folksonomy},
  year = 2006
}

@article{bille2005survey,
  author = {Bille, Philip},
  file = {bille2005survey.pdf:bille2005survey.pdf:PDF},
  interhash = {9de6a5b4195fd08c1ff901c2c7a12e9d},
  intrahash = {5099765c5f638e6aeb096dd1d0a44eb7},
  journal = {Theor. Comput. Sci.},
  lastdatemodified = {2007-04-15},
  lastname = {Bille},
  number = {1-3},
  own = {notown},
  pages = {217-239},
  read = {notread},
  title = {A survey on tree edit distance and related problems.},
  url = {http://dblp.uni-trier.de/db/journals/tcs/tcs337.html#Bille05},
  volume = 337,
  year = 2005
}

@inproceedings{ziegler2005improving,
  abstract = {In this work we present topic diversification, a novel method designed to balance and diversify personalized recommenda- tion lists in order to reflect the user�s complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with rec- ommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm. Our work builds upon prior research on recommender sys- tems, looking at properties of recommendation lists as en- tities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diver- sity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evalu- ate our method using book recommendation data, including online analysis on 361, 349 ratings and an online study in- volving more than 2, 100 subjects.},
  address = {Chiba, Japan},
  author = {Ziegler, Cai-Nicolas and McNee, Sean and Konstan, Joseph and Lausen, Georg},
  booktitle = {Proceedings of the 14th International World Wide Web Conference},
  file = {ziegler2005improving.pdf:ziegler2005improving.pdf:PDF},
  interhash = {0a7f89e65c4a0a5e45aa69a54a5600e6},
  intrahash = {1c70855a788c17e3a94a7ecc00177f6c},
  lastdatemodified = {2006-09-30},
  lastname = {Ziegler},
  month = May,
  own = {notown},
  pdf = {null},
  publisher = {ACM Press},
  read = {notread},
  title = {Improving Recommendation Lists Through Topic Diversification},
  year = 2005
}

@inproceedings{veres2006language,
  abstract = {Folksonomies are classification schemes that emerge from the collective actions of users who tag resources with an unrestricted set of key terms. There has been a flurry of activity in this domain recently with a number of high profile web sites and search engines adopting the practice. They have sparked a great deal of excitement and debate in the popular and technical literature, accompanied by a number of analyses of the statistical properties of tagging behavior. However, none has addressed the deep nature of folksonomies. What is the nature of a tag? Where does it come from? How is it related to a resource? In this paper we present a study in which the linguistic properties of folksonomies reveal them to contain, on the one hand, tags that are similar to standard categories in taxonomies. But on the other hand, they contain additional tags to describe class properties. The implications of the findings for the relationship between folksonomy and ontology are discussed.},
  address = {Berlin / Heidelberg},
  author = {Veres, Csaba},
  booktitle = {Natural Language Processing and Information Systems},
  file = {veres2006language.pdf:veres2006language.pdf:PDF},
  groups = {public},
  interhash = {1787dec43f3c11153fc9d2617af8829c},
  intrahash = {617763caa416f98b398cd2b2f71338ee},
  lastdatemodified = {2006-09-30},
  lastname = {Veres},
  month = {July},
  own = {notown},
  pages = {58-69},
  pdf = {veres06-language.pdf},
  publisher = {Springer},
  read = {notread},
  series = {Lecture Notes in Computer Science},
  timestamp = {2007-09-11 13:31:39},
  title = {The Language of Folksonomies: What Tags Reveal About User Classification.},
  url = {http://dx.doi.org/10.1007/11765448_6},
  username = {dbenz},
  volume = {3999/2006},
  year = 2006
}

@article{tonkin2006folksonomies,
  abstract = {A folksonomy is a type of distributed classification system. It is usually created by a group of individuals, typically the resource users. Users add tags to online items, such as images, videos, bookmarks and text. These tags are then shared and sometimes refined. In this article we look at what makes folksonomies work. We agree with the premise that tags are no replacement for formal systems, but we see this as being the core quality that makes folksonomy tagging so useful.},
  author = {Tonkin, Emma and Guy, Marieke},
  file = {tonkin2006folksonomies.pdf:tonkin2006folksonomies.pdf:PDF},
  interhash = {535e0aea1bcbd7feb85a7495f284a589},
  intrahash = {f56571b67b4e70a7d108dc8529d4c937},
  journal = {D-Lib},
  lastdatemodified = {2006-07-18},
  lastname = {Tonkin},
  location = {San Diego, California},
  month = {January},
  number = 1,
  own = {own},
  pdf = {tonkin06-folksonomies.pdf},
  read = {read},
  title = {Folksonomies: Tidying Up Tags?},
  url = {http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=2000478},
  volume = 12,
  year = 2006
}

@mastersthesis{dellschaft2005measuring,
  abstract = {The information available in corporate intranets and in the Internet grows from day to day. Looking for a specific information often the question is how to find it. Therefore it is the aim of researchers to allow a more efficient access to large collections of information. Many of the developed algorithms are dependent on additional domain knowledge for improving the achieved results (see (Gonzalo et al., 1998) and (De Buenaga Rodr�guez et al., 2000)). The domain knowledge is often available in the form of ontologies. An ontology reflects the understanding of a domain, on which a community has agreed upon. An ontology consists of different parts like a set of concepts and their mutual relations. These concepts are organized in a hierarchy of sub- and superconcepts. In order to actually improve the results of an application with the help of an ontology, it is crucial to accurately and exhaustively model the domain in question. Because this is a very complex and time consuming task it is a goal to extract an ontology at least semi-automatically. Such learning procedures use documents from the domain for extracting the necessary information. Often these documents are natural language texts like websites or dictionaries which contain domain knowledge (see (Kietz, Maedche and Volz, 2000) and (Cimiano, Hotho and Staab, 2004)). The quality of an automatically learned ontology is basically influenced by two parameters: The actual learning procedure and the document corpus. There exist several alternative learning procedures. They are further differentiated by the types of documents which they can process, i.e. whether they can process unstructured, semi-structured or structured documents. Websites are an example for unstructured documents, while dictionary entries and encyclopedia articles are examples for semi-structured documents. Documents containing artificial languages like database schemes are finally classified as structured documents. It is often assumed that the availability of structural information leads to a better quality of the extracted ontology. In order to enable a comparison of the different learning procedures, so that one can choose the best procedure for a certain purpose, they are often evaluated on an example corpus of documents. Subsequently it is tried to objectively measure the quality of the extracted ontology. Such an evaluation may also be used for fine tuning the parameters of a learning procedure, so that better results are achieved. One way of objectively evaluating a learning procedure is to measure the similarity between the learned ontology and a previously defined reference ontology. This similarity is then an equivalent for the quality. It is assumed that the learning procedure will always produce results with a comparable quality. This quality will only be influenced by the document corpus which must contain the correct informations.},
  address = {Germany},
  author = {Dellschaft, Klaas},
  dateadded = {2006-09-01},
  file = {dellschaft2005measuring.pdf:dellschaft2005measuring.pdf:PDF},
  interhash = {197543e8a02474709ffa0db4b9428d4f},
  intrahash = {46305dd6539f13b88dd7d288bc5dbab6},
  lastdatemodified = {2006-09-01},
  lastname = {Dellschaft},
  month = {December},
  own = {notown},
  pdf = {dellschaft05-measuring.pdf},
  read = {notread},
  school = {Institute for Computer Science, University of Koblenz-Landau},
  title = {Measuring the Similarity of Concept Hierarchies and its Influence on the Evaluation of Learning Procedures},
  url = {http://.uni-koblenz.de/FB4/Publications/Theses/ShowThesis?id=1908},
  year = 2005
}

@article{ziegler2006computing,
  author = {Ziegler, Cai Nicolas and Simon, Kai and Lausen, Georg},
  interhash = {74d9b7d218e52533d19ccacfd6d8e948},
  intrahash = {bd5647a471cc104c17726488c43fa7f3},
  journal = {Proceedings of the WWW2006},
  lastdatemodified = {2006-11-29},
  lastname = {Ziegler},
  note = {submitted},
  own = {notown},
  read = {notread},
  title = {Computing Semantic Proximity Between Concepts Using Taxonomic Knowledge},
  year = 2006
}

@inproceedings{yeh2006towards,
  abstract = {This paper discusses the automatic concept hierarchy generation process for specific knowledge network. Traditional concept hierarchy generation uses hierarchical clustering to group similar terms, and the result hierarchy is usually not satisfactory for human being recognition. Human-provided knowledge network presents strong semantic features, but this generation process is both labor-intensive and inconsistent under large scale hierarchy. The method proposed in this paper combines the results of specific knowledge network and automatic concept hierarchy generation, which produces a human-readable, semantic-oriented hierarchy. This generation process can efficiently reduce manual classification efforts, which is an exhausting task for human beings. An evaluation method is also proposed in this paper to verify the quality of the result hierarchy.},
  address = {Berlin / Heidelberg},
  author = {Yeh, Jian-Hua and hong Sie, Shun},
  booktitle = {Advances in Applied Artificial Intelligence},
  file = {yeh2006towards.pdf:yeh2006towards.pdf:PDF},
  interhash = {fb72d42a46d53453f4809f23a11d10e8},
  intrahash = {6b560e955077ba6d790082d37059e14d},
  lastdatemodified = {2006-09-30},
  lastname = {Yeh},
  month = {August},
  own = {notown},
  pages = {982--989},
  pdf = {yeh06-towards.pdf},
  publisher = {Springer},
  read = {notread},
  series = {Lecture Notes in Computer Science},
  title = {Towards Automatic Concept Hierarchy Generation for Specific Knowledge Network.},
  url = {http://dx.doi.org/10.1007/11779568_105},
  volume = 4031,
  year = 2006
}

@article{weiss2005power,
  author = {Weiss, Aaron},
  file = {weiss2005power.pdf:weiss2005power.pdf:PDF},
  interhash = {fd28e02b100a28c51606d604cf010693},
  intrahash = {c9326ac1288924b824ed1647b2b78062},
  journal = {netWorker},
  lastdatemodified = {2007-02-05},
  lastname = {Weiss},
  month = {September},
  number = 3,
  own = {notown},
  pages = {16--23},
  pdf = {weiss05-power.pdf},
  read = {notread},
  title = {The power of collective intelligence},
  url = {http://portal.acm.org/citation.cfm?id=1086763},
  volume = 9,
  year = 2005
}

@misc{sinha2005cognitive,
  author = {Sinha, Rashmi},
  interhash = {2aaeb38fd44f68a21ed54ecdb1a9b891},
  intrahash = {c12357f1eab7509961310a2ee377fa6e},
  lastdatemodified = {2007-04-27},
  lastname = {Sinha},
  own = {notown},
  read = {notread},
  title = {A cognitive analysis of tagging},
  url = {http://www.rashmisinha.com/archives/05_09/tagging-cognitive.html},
  year = 2005
}

@misc{shirky2005ontology,
  abstract = {Today I want to talk about categorization, and I want to convince you that a lot of what we think we know about categorization is wrong. In particular, I want to convince you that many of the ways we're attempting to apply categorization to the electronic world are actually a bad fit, because we've adopted habits of mind that are left over from earlier strategies. I also want to convince you that what we're seeing when we see the Web is actually a radical break with previous categorization strategies, rather than an extension of them. The second part of the talk is more speculative, because it is often the case that old systems get broken before people know what's going to take their place. (Anyone watching the music industry can see this at work today.) That's what I think is happening with categorization.},
  author = {Shirky, Clay},
  dateadded = {2006-07-17},
  file = {shirky2005ontology.pdf:shirky2005ontology.pdf:PDF},
  interhash = {064875980c48457bcc68c7abd003848c},
  intrahash = {78d52ca1bbb7290abbf47ce0d83b432b},
  lastdatemodified = {2006-07-17},
  lastname = {Shirky},
  month = May,
  own = {own},
  pdf = {shirky05-ontology.pdf},
  read = {read},
  title = {Ontology is Overrated: Categories, Links and Tags},
  url = {shirky.com},
  year = 2005
}

@misc{shen2005folksonomy,
  abstract = {Folksonomy is an emerging technology that works to classify the information over WWW through tagging the bookmarks, photos or other web-based contents. It is understood to be organized by every user while not limited to the authors of the contents and the professional editors. This study surveyed the folksonomy as a complex network. The result indicates that the network, which is composed of the tags from the folksonomy, displays both properties of small world and scale-free. However, the statistics only shows a local and static slice of the vast body of folksonomy which is still evolving.},
  author = {Shen, Kaikai and Wu, Lide},
  file = {shen2005folksonomy.pdf:shen2005folksonomy.pdf:PDF},
  interhash = {5150a1bb822f3ce4c888e2497eedbd07},
  intrahash = {0a5d7dfd17c6952fe7a07f7756098601},
  lastdatemodified = {2006-10-07},
  lastname = {Shen},
  month = Sep,
  own = {notown},
  pdf = {shen05-folksonomy.pdf},
  read = {notread},
  title = {Folksonomy as a Complex Network},
  url = {http://arxiv.org/abs/cs.IR/0509072},
  year = 2005
}

@inbook{schmitz2006kollaboratives,
  abstract = {Wissensmanagement in zentralisierten Wissensbasen erfordert einen hohen Aufwand f�r Erstellung und Wartung, und es entspricht nicht immer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen �berblick �ber zwei aktuelle Ans�tze, die durch kollaboratives Wissensmanagement diese Probleme l�sen k�nnen. Im Peer-to-Peer-Wissensmanagement unterhalten Benutzer dezentrale Wissensbasen, die dann vernetzt werden k�nnen, um andere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die Wissensakquisition so einfach wie m�glich zu gestalten und so viele Benutzer in den Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.},
  author = {Schmitz, Christoph and Hotho, Andreas and J�schke, Robert and Stumme, Gerd},
  editor = {Pellegrini, Tassilo and Blumauer, Andreas},
  file = {schmitz2006kollaboratives.pdf:schmitz2006kollaboratives.pdf:PDF},
  interhash = {a3102df5e75137fa4a95c718f470fd39},
  intrahash = {923e175b1912828ede540759dde1700a},
  lastdatemodified = {2007-04-27},
  lastname = {Schmitz},
  longnotes = {[[http://www.semantic-web.at/springer/abstracts/3d_Schmitz_KollabWM.pdf abstract (pdf)]]},
  own = {own},
  pages = {273-290},
  pdf = {schmitz06-kollaboratives.pdf},
  publisher = {Springer},
  read = {any},
  title = {Kollaboratives Wissensmanagement},
  year = 2006
}

@misc{resnik1995using,
  abstract = {This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r = 0.90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0.66).},
  author = {Resnik, Philip},
  file = {resnik1995using.pdf:resnik1995using.pdf:PDF},
  interhash = {746146003bcba4f1df57044178a1b9ac},
  intrahash = {454781d9c6deadeae45d0eba0d0cdf91},
  lastdatemodified = {2006-09-25},
  lastname = {Resnik},
  own = {notown},
  pdf = {resnik95-using.pdf},
  read = {notread},
  title = {Using Information Content to Evaluate Semantic Similarity in a Taxonomy},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cmp-lg/9511007},
  year = 1995
}

@techreport{page1998pagerank,
  author = {Page, Lawrence and Brin, Sergey and Motwani, Rajeev and Winograd, Terry},
  file = {page1998pagerank.pdf:page1998pagerank.pdf:PDF},
  institution = {Stanford Digital Library Technologies Project},
  interhash = {ca10cf0b0dd668c64b1f378ff0775849},
  intrahash = {408c27df50e9c4a8680426758f63656f},
  lastdatemodified = {2006-08-24},
  lastname = {Page},
  own = {notown},
  pdf = {page98-pagerank.pdf},
  read = {notread},
  title = {The PageRank Citation Ranking: Bringing Order to the Web},
  url = {citeseer.ist.psu.edu/page98pagerank.html},
  year = 1998
}