@book{leuf2001quick,
  address = {London},
  author = {Leuf, Bo and Cunningham, Ward},
  interhash = {7f9fb2b5bdcc9be84048552ed1ed6d04},
  intrahash = {28c210462bb61d92cbe1c4d31fe5dc30},
  isbn = {0-201-71499-X},
  month = mar,
  publisher = {Addison-Wesley},
  title = {The Wiki way: quick collaboration on the Web},
  year = 2001
}

@inproceedings{suchanek2007semantic,
  abstract = {We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in <i>quality</i> by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in <i>quantity</i> by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.},
  acmid = {1242667},
  address = {New York, NY, USA},
  author = {Suchanek, Fabian M. and Kasneci, Gjergji and Weikum, Gerhard},
  booktitle = {Proceedings of the 16th international conference on World Wide Web},
  doi = {10.1145/1242572.1242667},
  interhash = {1d2c2b23ce2a6754d12c4364e19c574c},
  intrahash = {84ae693c0a6dfb6d4b051b0b6dbd3668},
  isbn = {978-1-59593-654-7},
  location = {Banff, Alberta, Canada},
  numpages = {10},
  pages = {697--706},
  publisher = {ACM},
  title = {YAGO: a core of semantic knowledge},
  url = {http://doi.acm.org/10.1145/1242572.1242667},
  year = 2007
}

@article{luther2008situational,
  abstract = {We study the case of integrating situational reasoning into a mobile service recommendation system. Since mobile Internet services are rapidly proliferating, finding and using appropriate services require profound service descriptions. As a consequence, for average mobile users it is nowadays virtually impossible to find the most appropriate service among the many offered. To overcome these difficulties, task navigation systems have been proposed to guide users towards best-fitting services. Our goal is to improve the user experience of such task navigation systems making them context-aware (i.e. to optimize service navigation by taking the user's situation into account). We propose the integration of a situational reasoning engine that applies classification-based inference to qualitative context elements, gathered from multiple sources and represented using ontologies. The extended task navigator enables the delivery of situation-aware recommendations in a proactive way. Initial experiments with the extended system indicate a considerable improvement of the navigator's usability. },
  author = {Luther, Marko and Fukazawa, Yusuke and Wagner, Matthias and Kurakake, Shoji},
  doi = {10.1017/S0269888907001300},
  eprint = {http://journals.cambridge.org/article_S0269888907001300},
  interhash = {c71d15a53708c45d5911e4d9c940cd99},
  intrahash = {35ebbce0abbe9bbef462e5479cb419ed},
  issn = {1469-8005},
  journal = {The Knowledge Engineering Review},
  month = feb,
  number = {Special Issue 01},
  numpages = {13},
  pages = {7--19},
  title = {Situational reasoning for task-oriented mobile service recommendation},
  url = {http://dx.doi.org/10.1017/S0269888907001300},
  volume = 23,
  year = 2008
}

@inproceedings{baader2007completing,
  abstract = {We propose an approach for extending both the terminological and the assertional part of a Description Logic knowledge base by using information provided by the knowledge base and by a domain expert. The use of techniques from Formal Concept Analysis ensures that, on the one hand, the interaction with the expert is kept to a minimum, and, on the other hand, we can show that the extended knowledge base is complete in a certain, well-defined sense.},
  acmid = {1625311},
  address = {San Francisco, CA, USA},
  author = {Baader, Franz and Ganter, Bernhard and Sertkaya, Baris and Sattler, Ulrike},
  booktitle = {Proceedings of the 20th international joint conference on Artifical intelligence},
  interhash = {8ab382f3aa141674412ba7ad33316a9b},
  intrahash = {87f98ae486014ba78690ffa314b67da8},
  location = {Hyderabad, India},
  numpages = {6},
  pages = {230--235},
  publisher = {Morgan Kaufmann Publishers Inc.},
  title = {Completing description logic knowledge bases using formal concept analysis},
  url = {http://dl.acm.org/citation.cfm?id=1625275.1625311},
  year = 2007
}

@misc{weikum2011knowledge,
  author = {Weikum, Gerhard},
  interhash = {847973b9a334928684d6b4b88968867d},
  intrahash = {17076187ad6891c0cc1cdc252f3dbd80},
  month = nov,
  title = {Data and Knowledge Discovery},
  type = {expert paper},
  url = {http://151.1.219.218/6ccd3268-c29f-4f02-8442-75d9711825c0.pdf},
  year = 2011
}

@incollection{poelmans2010formal,
  abstract = {In this paper, we analyze the literature on Formal Concept Analysis (FCA) using FCA. We collected 702 papers published between 2003-2009 mentioning Formal Concept Analysis in the abstract. We developed a knowledge browsing environment to support our literature analysis process. The pdf-files containing the papers were converted to plain text and indexed by Lucene using a thesaurus containing terms related to FCA research. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. As a case study, we zoom in on the 140 papers on using FCA in knowledge discovery and data mining and give an extensive overview of the contents of this literature.},
  address = {Berlin/Heidelberg},
  author = {Poelmans, Jonas and Elzinga, Paul and Viaene, Stijn and Dedene, Guido},
  booktitle = {Conceptual Structures: From Information to Intelligence},
  doi = {10.1007/978-3-642-14197-3_15},
  editor = {Croitoru, Madalina and Ferré, Sébastien and Lukose, Dickson},
  interhash = {713d63f847ff4b2cbf613fc0508eb31b},
  intrahash = {9694689a034cc02aae1e27114ca26a94},
  isbn = {978-3-642-14196-6},
  pages = {139--153},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Formal Concept Analysis in Knowledge Discovery: A Survey},
  url = {http://dx.doi.org/10.1007/978-3-642-14197-3_15},
  volume = 6208,
  year = 2010
}

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

@incollection{fayyad1996data,
  abstract = {Data mining and knowledge discovery in
databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging
field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article
mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge
discovery, and current and future research directions in the field.},
  address = {Menlo Park, CA, USA},
  author = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic},
  booktitle = {Advances in knowledge discovery and data mining},
  editor = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic and Uthurusamy, Ramasamy},
  interhash = {79663e4b1f464b82ce1ae45345dc424f},
  intrahash = {3f5a400d01a974f993cee1ac5f79cfc8},
  isbn = {0-262-56097-6},
  pages = {1--34},
  publisher = {American Association for Artificial Intelligence},
  title = {From data mining to knowledge discovery: an overview},
  url = {http://portal.acm.org/citation.cfm?id=257942},
  year = 1996
}

@book{hayes-roth1983building,
  address = {Boston, MA, USA},
  author = {Hayes-Roth, Frederick and Waterman, Donald A. and Lenat, Douglas B.},
  interhash = {41c59cf56e936a3b1543a5f0cb5cb601},
  intrahash = {d442a324c5e3652b9856c6c20300580c},
  isbn = {0-201-10686-8},
  publisher = {Addison-Wesley Longman Publishing Co., Inc.},
  title = {Building expert systems},
  url = {http://portal.acm.org/citation.cfm?id=6123},
  year = 1983
}

@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
}

@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
}

@inproceedings{middleton01,
  abstract = {Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.},
  address = {New York, NY, USA},
  author = {Middleton, Stuart E. and Roure, David C. De and Shadbolt, Nigel R.},
  booktitle = {K-CAP '01: Proceedings of the 1st international conference on Knowledge capture},
  doi = {http://doi.acm.org/10.1145/500737.500755},
  interhash = {332dfc15a8f0fc442b47a9a4b740b1bf},
  intrahash = {6d0a7792db2c0f96bd0a495a56e57464},
  isbn = {1-58113-380-4},
  location = {Victoria, British Columbia, Canada},
  pages = {100--107},
  publisher = {ACM},
  title = {Capturing knowledge of user preferences: ontologies in recommender systems},
  url = {http://portal.acm.org/citation.cfm?id=500737.500755},
  year = 2001
}

@inproceedings{felfernig05,
  address = {Washington, DC, USA},
  author = {Felfernig, Alexander},
  booktitle = {CEC '05: Proceedings of the Seventh IEEE International Conference on E-Commerce Technology},
  doi = {http://dx.doi.org/10.1109/ICECT.2005.57},
  interhash = {017b5a7a234a9d2cbd5d2c6b459edd63},
  intrahash = {68c17752ba4ef2cec9bd515304fc4a95},
  isbn = {0-7695-2277-7},
  pages = {92--100},
  publisher = {IEEE Computer Society},
  title = {Koba4MS: Selling Complex Products and Services Using Knowledge-Based Recommender Technologies},
  url = {http://portal.acm.org/citation.cfm?id=1097216},
  year = 2005
}

@article{macgregor2006collaborative,
  abstract = {The purpose of the paper is to provide an overview of the collaborative tagging phenomenon and explore some of the reasons for its emergence. Design/methodology/approach - The paper reviews the related literature and discusses some of the problems associated with, and the potential of, collaborative tagging approaches for knowledge organisation and general resource discovery. A definition of controlled vocabularies is proposed and used to assess the efficacy of collaborative tagging. An exposition of the collaborative tagging model is provided and a review of the major contributions to the tagging literature is presented. Findings - There are numerous difficulties with collaborative tagging systems (e.g. low precision, lack of collocation, etc.) that originate from the absence of properties that characterise controlled vocabularies. However, such systems can not be dismissed. Librarians and information professionals have lessons to learn from the interactive and social aspects exemplified by collaborative tagging systems, as well as their success in engaging users with information management. The future co-existence of controlled vocabularies and collaborative tagging is predicted, with each appropriate for use within distinct information contexts: formal and informal. Research limitations/implications - Librarians and information professional researchers should be playing a leading role in research aimed at assessing the efficacy of collaborative tagging in relation to information storage, organisation, and retrieval, and to influence the future development of collaborative tagging systems. Practical implications - The paper indicates clear areas where digital libraries and repositories could innovate in order to better engage users with information. Originality/value - At time of writing there were no literature reviews summarising the main contributions to the collaborative tagging research or debate.},
  author = {Macgregor, George and McCulloch, Emma},
  doi = {10.1108/00242530610667558},
  editor = {McMenemy, David},
  interhash = {8d7a458fb6f9ff722c7d02104ec6dbd0},
  intrahash = {97d3915ead822cfa033fc821b424e437},
  issn = {0024-2535},
  journal = {Library Review},
  number = 5,
  pages = {291-300},
  publisher = {Emerald Group Publishing Limited},
  title = {Collaborative Tagging as a Knowledge Organisation and Resource Discovery Tool},
  url = {http://www.emeraldinsight.com/10.1108/00242530610667558},
  volume = 55,
  year = 2006
}

@inproceedings{conf/ht/WuZM06,
  author = {Wu, Harris and Zubair, Mohammad and Maly, Kurt},
  booktitle = {Hypertext},
  crossref = {conf/ht/2006},
  date = {2006-09-28},
  editor = {Wiil, Uffe Kock and Nürnberg, Peter J. and Rubart, Jessica},
  ee = {http://doi.acm.org/10.1145/1149941.1149962},
  interhash = {ea6aa5db3724812d08347d5a8309bea4},
  intrahash = {4b0512091911843390f88699d3ea3bb9},
  isbn = {1-59593-417-0},
  pages = {111-114},
  publisher = {ACM},
  title = {Harvesting social knowledge from folksonomies.},
  url = {http://dblp.uni-trier.de/db/conf/ht/ht2006.html#WuZM06},
  year = 2006
}

@techreport{carotenuto1999communityspace,
  abstract = {In this paper we describe CommunitySpace, a component of a project to support voluntary, electronic communities of practice. We detail some of our design decisions, emphasizing issues of flexibility, diversity, and democracy. These design decisions will have impact upon the user interface to CommunitySpace, but they have much more immediate impact upon the architecture, representation, and dynamics of usage of the system. Our work is in the requirements and design phase, and we are interested in the comments of our peers on our evolving ideas.},
  author = {Carotenuto, Linda and Etienne, William and Fontaine, Michael and Friedman, Jessica and Muller, Michael and Newberg, Helene and Simpson, Matthew and Slusher, Jason and Stevenson, Kenneth},
  institution = {IBM Watson Research Center},
  interhash = {0b2fee4b78c51114478e698f0dc40b13},
  intrahash = {a0abdf3ed0cac548f4dd5f8e073b6314},
  month = {April},
  number = {99-04},
  title = {Community Space: Toward Flexible Support for Voluntary Knowledge Communities},
  year = 1999
}

@inproceedings{fischer2001communities,
  address = {Ulvik, Hardanger Fjord, Norway},
  author = {Fischer, Gerhard},
  booktitle = {24th annual Information Systems Research Seminar in Scandinavia},
  interhash = {790e778dcd713ffb84756a6734d1097a},
  intrahash = {2b58a1bff72a7440c43786fc4c1493b0},
  month = {August},
  pages = 2001,
  title = {Communities of Interest: Learning through the Interaction of Multiple Knowledge Systems},
  year = 2001
}

@techreport{hotho03textclustering,
  abstract = {Text document clustering plays an important role in providing intuitive
navigation and browsing mechanisms by organizing large amounts of information
into a small number of meaningful clusters. Standard partitional or agglomerative
clustering methods efficiently compute results to this end.
However, the bag of words representation used for these clustering methods is often
unsatisfactory as it ignores relationships between important terms that do not
co-occur literally. Also, it is mostly left to the user to find out why a particular partitioning
has been achieved, because it is only specified extensionally. In order to
deal with the two problems, we integrate background knowledge into the process of
clustering text documents.
First, we preprocess the texts, enriching their representations by background knowledge
provided in a core ontology — in our application Wordnet. Then, we cluster
the documents by a partitional algorithm. Our experimental evaluation on Reuters
newsfeeds compares clustering results with pre-categorizations of news. In the experiments,
improvements of results by background knowledge compared to the baseline
can be shown for many interesting tasks.
Second, the clustering partitions the large number of documents to a relatively small
number of clusters, which may then be analyzed by conceptual clustering. In our approach,
we applied Formal Concept Analysis. Conceptual clustering techniques are
known to be too slow for directly clustering several hundreds of documents, but they
give an intensional account of cluster results. They allow for a concise description
of commonalities and distinctions of different clusters. With background knowledge
they even find abstractions like “food” (vs. specializations like “beef” or “corn”).
Thus, in our approach, partitional clustering reduces first the size of the problem
such that it becomes tractable for conceptual clustering, which then facilitates the
understanding of the results.},
  author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd},
  comment = {alpha},
  institution = {University of Karlsruhe, Institute AIFB},
  interhash = {0bc7c3fc1273355f45c8970a7ea58f97},
  intrahash = {61d58db419af0dbc3681432588219c3d},
  title = {Text Clustering Based on Background Knowledge},
  type = {Technical Report },
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003text.pdf},
  volume = 425,
  year = 2003
}

@inbook{schmitz2006kollaboratives,
  abstract = {Wissensmanagement in zentralisierten Wissensbasen erfordert
einen hohen Aufwand für Erstellung und Wartung, und es entspricht nicht
immer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen Überblick
über zwei aktuelle Ansätze, die durch kollaboratives Wissensmanagement
diese Probleme lösen können. Im Peer-to-Peer-Wissensmanagement unterhalten
Benutzer dezentrale Wissensbasen, die dann vernetzt werden können, um
andere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die
Wissensakquisition so einfach wie möglich zu gestalten und so viele Benutzer in
den Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {Semantic Web - Wege zur vernetzten Wissensgesellschaft},
  editor = {Pellegrini, Tassilo and Blumauer, Andreas},
  interhash = {cc0f3d4fa8f36968f02837e3f9f5c57b},
  intrahash = {53e13744981f2c04d9239e0cf9b4e689},
  isbn = {3-540-29324-8},
  pages = {273-290},
  publisher = {Springer},
  title = {Kollaboratives Wissensmanagement},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006kollaboratives.pdf},
  year = 2006
}

@inproceedings{Lange:2006:ASW4MKM,
  author = {Lange, Christoph and Kohlhase, Michael},
  booktitle = {Proceedings of the First Workshop on Semantic Wikis -- From Wiki
	To Semantics},
  crossref = {SemWiki2006-proceedings},
  editor = {V\"{o}lkel, Max and Schaffert, Sebastian},
  interhash = {52fb2651a1376b7c11c0375ed91658f9},
  intrahash = {09499c09e4c9b63f2d881ecdf13795bf},
  month = {June},
  owner = {voelkel},
  publisher = {ESWC2006},
  series = {Workshop on Semantic Wikis},
  timestamp = {2006.06.14},
  title = {A Semantic Wiki for Mathematical Knowledge Management},
  url = {http://semwiki.org/semwiki2006},
  year = 2006
}